{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71e6d8fe-84c4-47d7-8fd7-f6a66fed72e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import os\n",
    "import json\n",
    "import operator\n",
    "import pandas as pd\n",
    "from io import StringIO\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "# settings\n",
    "BotHash = \"BEEB1E1549DDD01226DC2FC912ABAE49\"\n",
    "BotName = \"PokerShark2\"\n",
    "SB = 10\n",
    "\n",
    "# players\n",
    "Players = ['PokerShark1']\n",
    "\n",
    "# helper functions\n",
    "def prettyPrint(*arguments):\n",
    "    out = ''\n",
    "    for arg in arguments:\n",
    "        space = (30-len(arg)) * \" \"\n",
    "        out += arg + space\n",
    "    print(out)\n",
    "\n",
    "# gets an array of paths to player games\n",
    "def getGamesPath(playerName):\n",
    "    return [os.path.join(playerName,f) for f in os.listdir(playerName) if not f.startswith('.')]\n",
    "\n",
    "# Load game as json object.\n",
    "def loadGame(path):\n",
    "    with open(path) as f:\n",
    "        g = json.load(f)\n",
    "        # Throw if game not logged by the current version of the bot\n",
    "        if g[\"BotHash\"] != BotHash:\n",
    "            #raise Exception(\"BotHash does not match: \" + g[\"BotHash\"])\n",
    "            pass\n",
    "        return g\n",
    "\n",
    "# game statistics\n",
    "def getGamesCount(playerName):\n",
    "    # returns number of games played against the given player.\n",
    "    return len(getGamesPath(playerName))\n",
    "\n",
    "def getGameResultForPlayer(game, playerName):\n",
    "    # returns game result for given player.\n",
    "    results = game[\"Results\"]\n",
    "    maxStack = 0\n",
    "    hasMax = []\n",
    "    # find max stack\n",
    "    for result in results:\n",
    "        if maxStack < result[\"Stack\"]:\n",
    "            maxStack = result[\"Stack\"]\n",
    "            hasMax= [result[\"Player\"][\"Name\"]]\n",
    "        elif maxStack == result[\"Stack\"]:\n",
    "            hasMax.append(result[\"Player\"][\"Name\"])\n",
    "    # find player result\n",
    "    if playerName in hasMax:\n",
    "        if len(hasMax) > 1:\n",
    "            return 'D'\n",
    "        else:\n",
    "            return 'W'\n",
    "    return 'L'\n",
    "\n",
    "def getWonGamesCount(playerName):\n",
    "    # returns number of games won against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        result = getGameResultForPlayer(game, BotName)\n",
    "        if result == 'W':\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "def getDrewGamesCount(playerName):\n",
    "    # return number of games drew against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        result = getGameResultForPlayer(game, BotName)\n",
    "        if result == 'D':\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "def getLostGamesCount(playerName):\n",
    "    # return number of games lost against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        result = getGameResultForPlayer(game, BotName)\n",
    "        if result == 'L':\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "def getEndStack(playerName):\n",
    "    # return sum of the stack after the games.\n",
    "    s = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for result in game[\"Results\"]:\n",
    "            if result[\"Player\"][\"Name\"] == BotName:\n",
    "                s += result[\"Stack\"]\n",
    "    return s\n",
    "            \n",
    "# round statistics\n",
    "def getRoundsCount(playerName):\n",
    "    # returns number of rounds played against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        count += len(game[\"Rounds\"])\n",
    "    return count\n",
    "\n",
    "def GetRoundResultForPlayer(r, playerName):\n",
    "    results = r[\"Winner\"]\n",
    "    winners = len(results)\n",
    "    won = False\n",
    "    for result in results:\n",
    "        if playerName == result[\"Name\"]:\n",
    "            won = True\n",
    "            break\n",
    "    if won:\n",
    "        if winners > 1:\n",
    "            return 'D'\n",
    "        else:\n",
    "            return 'W'\n",
    "    return 'L'\n",
    "\n",
    "def getWonRoundsCount(playerName):\n",
    "    # returns number of rounds won against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for r in game[\"Rounds\"]:\n",
    "            result = GetRoundResultForPlayer(r, BotName)\n",
    "            if result == 'W':\n",
    "                count += 1\n",
    "    return count\n",
    "\n",
    "def getDrewRoundsCount(playerName):\n",
    "    # return number of games rounds against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for r in game[\"Rounds\"]:\n",
    "            result = GetRoundResultForPlayer(r, BotName)\n",
    "            if result == 'D':\n",
    "                count += 1\n",
    "    return count\n",
    "\n",
    "def getLostRoundsCount(playerName):\n",
    "    # return number of rounds lost against the given player.\n",
    "    count = 0\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for r in game[\"Rounds\"]:\n",
    "            result = GetRoundResultForPlayer(r, BotName)\n",
    "            if result == 'L':\n",
    "                count += 1\n",
    "    return count\n",
    "\n",
    "def endedBeforeShowdown(r):\n",
    "    # returns true if one of the two players folded.\n",
    "    for action in r[\"History\"]:\n",
    "        if action[\"Type\"] == \"Fold\":\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def botFolded(r):\n",
    "    # returns true if one of the two players folded.\n",
    "    for action in r[\"History\"]:\n",
    "        if action[\"Type\"] == \"Fold\" and action[\"PlayerName\"] == BotName:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def getPaid(r,playerName):\n",
    "    # returns amount paid by player\n",
    "    amount = 0\n",
    "    # blinds\n",
    "    sbp = r['SmallBlindPosition']\n",
    "    for p in range(len(r['Players'])):\n",
    "        if r['Players'][p]['Name'] == playerName:\n",
    "            if p == sbp:\n",
    "                amount += SB\n",
    "            else:\n",
    "                amount += SB + SB\n",
    "    # calls and raises\n",
    "    for action in r[\"History\"]:\n",
    "        if action[\"Type\"] != \"Fold\" and action[\"PlayerName\"] == playerName:\n",
    "            amount += action[\"Amount\"]\n",
    "    return amount\n",
    "\n",
    "# winning statistics\n",
    "def getNonShowdownWinningsLine(playerName):\n",
    "    # line goes down each time bot fold, and up each time your opponent fold.\n",
    "    x = [0]\n",
    "    y = [0]\n",
    "    line = 0\n",
    "    count = 1\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for r in game[\"Rounds\"]:\n",
    "            if endedBeforeShowdown(r):\n",
    "                if botFolded(r):\n",
    "                    line -= getPaid(r,BotName)\n",
    "                else:\n",
    "                    line += getPaid(r,playerName)\n",
    "            x.append(count)\n",
    "            y.append(line)\n",
    "            count += 1\n",
    "    return (x,y)\n",
    "\n",
    "def getShowdownWinningsLine(playerName):\n",
    "    # line goes down each time bot fold, and up each time your opponent fold.\n",
    "    x = [0]\n",
    "    y = [0]\n",
    "    line = 0\n",
    "    count = 1\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for r in game[\"Rounds\"]:\n",
    "            if not endedBeforeShowdown(r):\n",
    "                # if bot won\n",
    "                if GetRoundResultForPlayer(r, BotName) == 'W':\n",
    "                    line += getPaid(r,playerName)\n",
    "                if GetRoundResultForPlayer(r, BotName) == 'L':\n",
    "                    line -= getPaid(r,BotName)\n",
    "                \n",
    "            x.append(count)\n",
    "            y.append(line)\n",
    "            count += 1\n",
    "    return (x,y)\n",
    "\n",
    "def getStackLine(playerName):\n",
    "    # line goes down each time bot fold, and up each time your opponent fold.\n",
    "    x = [0]\n",
    "    y = [0]\n",
    "    line = 0\n",
    "    count = 1\n",
    "    files = getGamesPath(playerName)\n",
    "    for file in files:\n",
    "        game = loadGame(file)\n",
    "        for r in game[\"Rounds\"]:\n",
    "            if GetRoundResultForPlayer(r, BotName) == 'W':\n",
    "                line += getPaid(r,playerName)\n",
    "            if GetRoundResultForPlayer(r, BotName) == 'L':\n",
    "                line -= getPaid(r,BotName)\n",
    "            x.append(count)\n",
    "            y.append(line)\n",
    "            count += 1\n",
    "    return (x,y)\n",
    "\n",
    "def plotWinnings(playerName):\n",
    "    # NonShowdownWinningsLine\n",
    "    x,y = getNonShowdownWinningsLine(playerName)\n",
    "    fig, ax = plt.subplots()\n",
    "    xpoints = np.array(x)\n",
    "    ypoints = np.array(y)\n",
    "    ax.plot(xpoints, ypoints,linewidth=1,label='Non-showdown winnings', c='r')\n",
    "    # ShowdownWinningsLine\n",
    "    x,y = getShowdownWinningsLine(playerName)\n",
    "    xpoints = np.array(x)\n",
    "    ypoints = np.array(y)\n",
    "    ax.plot(xpoints, ypoints,linewidth=1,label='Showdown winnings', c='b')\n",
    "    # Stack\n",
    "    x,y = getStackLine(playerName)\n",
    "    xpoints = np.array(x)\n",
    "    ypoints = np.array(y)\n",
    "    ax.plot(xpoints, ypoints,linewidth=1,label='Winnings', c='g')\n",
    "    # show plot\n",
    "    y_max = np.abs(ax.get_ylim()).max()\n",
    "    ax.set_ylim(ymin=-y_max, ymax=y_max)\n",
    "    ax.set_xlim(left=0)\n",
    "    plt.legend()\n",
    "    plt.grid(linestyle = '--', linewidth = 0.5)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "# Print Results\n",
    "def PrintResult():\n",
    "    for player in Players:\n",
    "        prettyPrint(\"Results against:\", player)\n",
    "        # game statistics\n",
    "        n = getGamesCount(player)\n",
    "        w = getWonGamesCount(player)\n",
    "        d = getDrewGamesCount(player)\n",
    "        l = getLostGamesCount(player)\n",
    "        prettyPrint(\"number of games:\",str(n))\n",
    "        prettyPrint(\"number of games won:\", str(w) , \"{:.1f}\".format(w*100/n) + \"%\")\n",
    "        prettyPrint(\"number of games drew:\", str(d), \"{:.1f}\".format(d*100/n) + \"%\")\n",
    "        prettyPrint(\"number of games lost:\", str(l), \"{:.1f}\".format(l*100/n) + \"%\")\n",
    "        # round statistics\n",
    "        rn = getRoundsCount(player)\n",
    "        rw = getWonRoundsCount(player)\n",
    "        rd = getDrewRoundsCount(player)\n",
    "        rl = getLostRoundsCount(player)\n",
    "        prettyPrint(\"number of rounds:\",str(rn))\n",
    "        prettyPrint(\"number of rounds won:\", str(rw) , \"{:.1f}\".format(rw*100/rn) + \"%\")\n",
    "        prettyPrint(\"number of rounds drew:\", str(rd), \"{:.1f}\".format(rd*100/rn) + \"%\")\n",
    "        prettyPrint(\"number of rounds lost:\", str(rl), \"{:.1f}\".format(rl*100/rn) + \"%\")\n",
    "        # winnings\n",
    "        prettyPrint(\"starting stack:\", str(1000 * n))\n",
    "        prettyPrint(\"end stack:\", str(getEndStack(player)))\n",
    "        prettyPrint(\"win/loss:\", str(getEndStack(player) - (1000 * n)  ))\n",
    "        prettyPrint(\"WPH:\", str((getEndStack(player) - (1000 * n)) / (rn*10)))\n",
    "        plotWinnings(player)\n",
    "        # end\n",
    "        print('-------------------------------------------------------------')\n",
    "        \n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f45a61bb-8373-4bd1-8fd6-b85a124517bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dynamic player:\n",
      "Results against:              PokerShark2                   \n",
      "number of games:              100                           \n",
      "number of games won:          10                            10.0%                         \n",
      "number of games drew:         0                             0.0%                          \n",
      "number of games lost:         90                            90.0%                         \n",
      "number of rounds:             9906                          \n",
      "number of rounds won:         3545                          35.8%                         \n",
      "number of rounds drew:        7                             0.1%                          \n",
      "number of rounds lost:        6354                          64.1%                         \n",
      "starting stack:               100000                        \n",
      "end stack:                    67070.0                       \n",
      "win/loss:                     -32930.0                      \n",
      "WPH:                          -0.33242479305471434          \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------\n",
      "risk-seeking player:\n",
      "Results against:              PokerShark1                   \n",
      "number of games:              100                           \n",
      "number of games won:          90                            90.0%                         \n",
      "number of games drew:         0                             0.0%                          \n",
      "number of games lost:         10                            10.0%                         \n",
      "number of rounds:             9906                          \n",
      "number of rounds won:         6354                          64.1%                         \n",
      "number of rounds drew:        7                             0.1%                          \n",
      "number of rounds lost:        3545                          35.8%                         \n",
      "starting stack:               100000                        \n",
      "end stack:                    132930.0                      \n",
      "win/loss:                     32930.0                       \n",
      "WPH:                          0.33242479305471434           \n"
     ]
    },
    {
     "data": {
      "image/png": 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JTU0lMTGRP/7445THgyOprpAU9aNRZ//XX0YBtGaNcQv+0aPw7LOVxVEDoDN/KZBEFU8++WSVUx+xsbH873//Y968eURHR/Poo4/yxBNPVDkVV1Nubm488MADdOvWjQEDBuDs7My8efPO+PVn0i+LxWKOqFQUK926dcPf358ePXrYFCtXX301jz76KPfddx9xcXHs3buX22+/3eY9p0yZwowZM5gxYwZdu3Zl4cKF5u3rFaKjowkODjZHd8AYtSovL6+10Z1BgwZRXl5uFkMVo1SATdHXULRp04ZvvvmGb7/9lm7dujFnzhweeughANzd3QHjWqdJkybRqVMnRowYQceOHXnrrbd0dlsI+1Zx2/2BA5CVBZ99Bn5+xtmNiu+JH36A3FwIDtba1QanVi4PPwOzZs1SgJo6daq5zWq1qpkzZ6qwsDDl4eGh4uPjVXJyss3rioqK1OTJk1VQUJDy8vJSo0aNMm9Fr5CRkaFuuOEG5efnp/z8/NQNN9xg3hVTYe/eveqiiy5SXl5eKigoSN15552quLi4Rp+hMd7mb0+Kiop0d8Fh1VX2Tz31lGrRosVZvdaR/s7t3btXdxcclt1mn5qq1DnnVN59dvJP795KxccrtXat7p6eVm3lfzZ3sdXLCFJCQgLvvvtulbtRnnvuOV566SXeeOMNEhISCA0NZejQoeTm5pptpk2bxnfffce8efNYsWIFeXl5XHTRRZSXl5ttrrvuOpKSkli4cCELFy4kKSmJMWPGmM+Xl5czcuRI8vPzWbFiBfPmzWP+/PnV3q0lGi5HWr29oamt7N966y0SEhLYvXs3n332Gc8//zw33XRTrey7MZMZy/Wxu+yVgmeeMZbyWLnSmNCxQrt2xoSOViusXm3ctabjTusa0Jp/rZRmp5Gbm6siIyPVb7/9puLj480RJKvVqkJDQ9Uzzzxjti0qKlL+/v7q7bffVkoplZWVpVxdXdW8efPMNgcPHlROTk5q4cKFSimltmzZogD1999/m21WrVqlALV161allFI///yzcnJyUgcPHjTbzJ07V7m7u9eompQRJL0q5ioS9a+2sp82bZoKCwtT7u7uKjIyUj3xxBPmpJ815Uh/5078fhP1q0FnX3HsW61Kvfaa7QhR+/ZK2evo1wlqK/8GOYI0adIkRo4cyZAhQ2y2p6amkpaWxrBhw8xt7u7uxMfHs3LlSgDWrVtHaWmpTZvw8HCio6PNNqtWrcLf358+ffqYbfr27Yu/v79Nm+joaMLDw802w4cPp7i4mHWnuQ2/uLiYnJwcmx8hxNl7+eWXOXToEEVFRWzfvp1HHnlEFgMVoqaWLDHukvb0rFwcdsoU47lrroH/+z/YsQNatdLbTztXp99M8+bNIzExkYSEhCrPpaWlAdCsWTOb7c2aNWPv3r1mGzc3NwICAqq0qXh9WloaISEhVfYfEhJi0+bk9wkICMDNzc1sU53Zs2fz+OOPV9m+du1avL29iY2NJSUlhcLCQry9vXFyciI/P5+ysjLc3d1RSpm3pXt5eVFcXGxOXuju7k5BQQFgXKhssVjMBUFPbOvk5ISnp6d5F9HJbT09PSktLaWsrKxKW1dXV5ydnc21xE5sa7FY8Pb2Jj8/H6VUlbYeHh6Ul5dTWlpapa2Liwuurq7mnEYntgXw8fE5ZVt3d3esVqvZ1tvbm8LCQqxWK87Ozri5udm0PTFDDw8PCgoKzLYnZwhUm7eTk5P52lNlWFJSUm3erq6uODk5VZt3RS55eXla8z4xw+ryPjHDk/M+McPTHbNOTk5YrdYzPmb/Le/THbNnmrfVasVqtZoTdEZFRXHs2DGOHTuGk5MTvXr1IiEhAavVSnBwMMHBwWzduhWAyMhIsrOzzUks+/TpQ2JiIqWlpeZ6eFu2bAGgXbt25Ofnm98VvXr1YuPGjRQXF9OkSRNatmxpTqDZpk0bSkpKOHjwIIDNd4Svry9t2rRh48aNgDFZqtVqZf/+/QDExMSwc+dO8vLy8Pb2pkOHDqz/Z/HP4OBg0tLSzO/Gbt26sWfPHnJycvDw8KBLly7mP/bCw8Px8PBg9+7dgHHDwIEDB8jKysLNzY2YmBjW/HNLd2hoKD4+PuzcuRMwprk4cuQIGRkZuLi4EBcXx5o1a1BK0bRpUwICAti+fTsAHTt2JCMjg6NHj5p5r127lvLycoKCgggJCSElJcXMOycnhyNHjlTJOyAggPDwcPNu0Xbt2lFQUGCuDdizZ0+Sk5MpKirC39+fVq1amXlHRERQVlbGgQMHzLy3bt1KQUEBPj4+tGvXzjw+Wv1TLOzbtw+A7t27s2vXLvLy8vDy8iIqKorExEQAWrRogYuLC3v27KGsrIyCggL27dtHdnY2Hh4eREdHm3dXhYWF4eXlxa5duwBjgtVDhw6RmZmJq6srsbGxrF69GjB+d/n5+bFjxw4z7/T0dI4fP46zszM9e/Y0j9mmTZsSGBjItq1bQSli3n4b908/5chll9Hs2285WdmgQWy+/XaKWrWiffv25O3dax6zvXv3JikpiZKSEpo0aUKLFi1ITk4GoG3bthQVFXHo0CEA4uLi2Lx5M0VFRfj5+REREWFzzJaXl5t59+jRg+3bt5Ofn4+Pjw/t27cnKSkJMKbrcHJysjlmU1NTyc3NxdPTk06dOpl5N2/eHDc3N1JTUwHjbur9+/eTlZVlfvdU1BGhoaF4e3ubeXfu3Jm0tDQyMjKq5B0SEoK/vz87duw4uztxa2Xsqhr79u1TISEhKikpydx24im2v/76SwHq0KFDNq+77bbb1PDhw5VSSn3xxRfKzc2tyr6HDBmiJkyYoJRS6umnn1YdOnSo0qZ9+/Zq9uzZSimlxo0bp4YNG1aljaurq5o7d+4pP0NRUZHKzs42f/bv3y+n2DSSbPVpiNk70t+5bdu26e6Cw9Ka/ddfV3+BdWCgUqWlxqm1goI6Xw9Np9rKv0GdYlu3bh3p6enExcXh4uKCi4sLy5Yt47XXXsPFxcUc0Tl5BCc9Pd18LjQ0lJKSEjIzM0/bpuJfJSc6evSoTZuT3yczM5PS0tIqI0sncnd3x8/Pz+ZH6FNWVqa7Cw5Lstfr5O9AUX+0ZB8XZ5w6u/LKym2//gqpqfDFF7B9O7i4GG0qTrM1UjqP/TorkAYPHsymTZtISkoyf3r27Mn1119PUlISbdu2JTQ0lN9++818TUlJCcuWLTPX4oqLi8PV1dWmzeHDh0lOTjbb9OvXj+zsbHPIGGD16tVkZ2fbtElOTjaHbAEWLVqEu7s7cQ38Cn5R6eRlKkT9kez1OnkJF1F/6iV7pYz5ih58EAYMgH9OPdGnDxw5Yjw/bBhERMB110FQUN33qYHQeezX2TVIvr6+VaYI9/b2JigoyNw+bdo0Zs2aRWRkJJGRkcyaNQsvLy+uu+46APz9/bn11luZMWMGQUFBBAYGcvfdd9O1a1fzou+KCeXGjRvHO++8A8D48eO56KKL6NixIwDDhg2jc+fOjBkzhueff56MjAzuvvtuxo0bJ6NCdsTb21t3FxyWZK9XrJ0tWdSY1Hn2qanQtq3tth494O234aTFnB2RzmNf68Qy9957L9OmTeOOO+6gZ8+eHDx4kEWLFtkssvnyyy8zevRorrrqKs4991y8vLz44YcfbNb/+uKLL+jatSvDhg1j2LBhdOvWjc8++8x83tnZmZ9++gkPDw/OPfdcrrrqKkaPHl3tel+i4aq4OFfUP8ler4qLTkX9q7Psi4uha9fK4ig2FubMMUaMEhOlOPqHzmPfolQ1S7KLauXk5ODv7092dnaVkaeioiJSU1Np06ZNo1v92WKx8N133zF69Ghtffj444+ZNm0aWVlZ2vrwX+zZs4c2bdqwfv16YmJiam2/AwcOJCYmhldeeaXW9lmdvLw8fHx86vQ9aqox/5072erVq22mMhH1p1azT0+Hd9+FRx6x3b59O5ywVJGoVFv5n+7396nI1MQOLj09nQkTJtCqVSvc3d0JDQ1l+PDhrFq1SnfXGpWWLVty+PDhWl+Z+ttvv+XJJ5+s1X1WR66B0et0N5OIuvWfs8/NNWa2tligWTPb4ujTT41ZraU4OiWdx77M0ObgLr/8ckpLS/nkk09o27YtR44c4ffffycjI0N31xoVZ2dnQkNDa32/gYGBtb7P6px4SlvUP7lWUp//lH1SEsTHGxdgA9x0E0yfDictuyVOTeexLyNIDiwrK4sVK1bw7LPPMmjQIFq3bk3v3r154IEHGDlypE3bY8eOcemll+Ll5UVkZCTff/+9zfPLli2jd+/euLu7ExYWxv3332/eGv7DDz/QpEkTrFYrAElJSVgsFu655x7z9RMmTODaa681H3/88ce0atUKLy8vLr30Uo4fP16l/3PmzKFdu3a4ubnRsWNHm+vOZsyYwahRo8zHr7zyChaLhZ9++snc1rFjR/PC/rFjx5rXpYWFhREUFMSkSZPMyRhPlp2djbOzszk5n1KKwMBAevXqZbaZO3cuYf+sg7Rnzx4sFos5idrSpUuxWCz8/vvv9OzZEy8vL8455xy2bdtmvv6xxx4jJiaGzz77jIiICPz9/bnmmmts1iocOHAg06ZNMx9HREQwa9YsbrnlFnx9fWnVqhXvvvuuTd9XrlxJTEwMHh4e9OzZkwULFtj0LTMzk+uvv56mTZvi6elJZGQk7733XrU5iPpRMbGgqH9nnL1S8OGHRkF0443QpYtxsbWHB6xaZTz/8cdSHNWQzmNfCiQH5uPjg4+PDwsWLDBnLj6Vxx9/nKuuuoqNGzdy4YUXcv3115ujTAcPHuTCCy+kV69ebNiwgTlz5vDBBx/w1FNPATBgwAByc3PNWYGXLVtGcHAwy5YtM/e/dOlS4uPjAeOc8y233MIdd9xBUlISgwYNMvdV4bvvvmPq1KnMmDGD5ORkJkyYwM0338ySJUsAo3BYvny5WZSd/J5paWls377dfE+AJUuWsGvXLpYsWcInn3zCxx9/zMcff1xtHv7+/sTExLB06VIAc6bZjRs3mkvSnPiZTuWhhx7ixRdfZO3atbi4uHDLLbfYPL9r1y4WLFjAjz/+yI8//siyZct45plnTrvPF198kZ49e7J+/XruuOMObr/9dnMG6dzcXEaNGkXXrl1JTEzkySef5L777rN5/SOPPMKWLVv45ZdfSElJYc6cOQQ50G3FQtSYUjB8ONx6K/z5J3z2GWzZYiwOu3Qp9O2ru4fibNTKFJUOoqaL1ebnK7VuXf3/5Oef+Wf65ptvVEBAgPLw8FDnnHOOeuCBB9SGDRts2gDq4YcfNh/n5eUpi8WifvnlF6WUUg8++KDq2LGjsp4wm+ubb76pfHx8VHl5uVJKqdjYWPXCCy8opZQaPXq0evrpp5Wbm5vKyclRhw8fVoBKSUlRSil17bXXqhEjRtj04eqrr1b+/v7m43POOUeNGzfOps2VV16pLrzwQqWUsdCxk5OTWrt2rbJarSooKEjNnj1b9erVSyml1JdffqmaNWtmvvamm25SrVu3VmVlZTb7u/rqq0+Z3fTp09VFF12klFLqlVdeUVdccYWKjY1VP/30k1JKqQ4dOqg5c+YopZRKTU1VgFq/fr1SSqklS5YoQC1evNjc308//aQA8xiaOXOm8vLyUjk5OWabe+65R/Xp08d8fOLs9Eop1bp1a3XDDTeYj61WqwoJCTH7MWfOHBUUFGRznL733ns2fRs1apS6+eabbT7ribk0FI40k3ZNZv8Vtava7PPylFqxQqkBA5QaPVqpwYONGa6HDlVqwwalHn9cqQMH6r+zjVBtHftnM5O2XINUh7ZuNSZErW/r1hl3jJ6Jyy+/nJEjR7J8+XJWrVrFwoULee6553j//fcZO3as2a7bCcPC3t7e+Pr6mutYpaSk0K9fP5vJBM8991zy8vI4cOAArVq1YuDAgSxdupTp06ezfPlynnrqKebPn8+KFSvIysqiWbNmREVFmfu79NJLbfrZr18/Fi5caD5OSUlh/PjxNm3OPfdcXn31VcB2hKdifa8JEyYwc+ZMcnNzqx3d6dKli821NmFhYeaaT9UZOHAgH3zwAVarlWXLljF48GBatWrFsmXLiI2NrTJCVZ0Tc604HZeenm6uGxUREWEz7UVYWJiZ+5ns02KxEBoaar5m27ZtdOvWzeaur94n3U58++23c/nll5OYmMiwYcMYPXo0sbGxch2SRunp6XIdkiY22b/0EsyYUX3DgQPhp5/A1VVOo52Ffdn7cLY409yvuc12nce+FEh1KCrKKFZ0vG9NeHh4MHToUIYOHcqjjz7KbbfdxsyZM20KpJPvYrJYLObpK6VUlZmW1T+zR1RsrygmNmzYgJOTE507dyY+Pp5ly5aRmZlpU0ioM5x5orr3PHFbRVHm5uZGfHw8AQEBdOnShb/++oulS5faXLvzb5+xOhWnDhMTE1m+fDlPPvkkLVu2ZNasWcTExBASEkKnTp1O+xlOfM+Kvp/4njXt07+95nT/rypccMEF7N27l59++onFixczePBgxo0bx2uvvXba9xV15/jx47Rv3153NxzS8ePHaZ+XB6NHwz8LrwLGdUY332wsAXLNNdC9u7Y+2rOsoixGfD6C1QeN+Y6sj1ptvqN0HvtSINUhL68zH8lpSDp37syCBQtq1H7+/Pk2v3xXrlyJr68vzZsb/xqoKCZeeeUV4uPjsVgsxMfHM3v2bDIzM5k6darN/v7++2+b9zj5cadOnVixYgU33nijuW3lypU2BUlFUebi4mLOvB4fH8+8efPOaHTn31SMUr3xxhtYLBY6d+5MeHg469ev58cff/zP+68LUVFRfPHFFxQXF+Pu7g5grkp+oqZNmzJ27FjGjh1L//79ueeee6RA0khG7zTIyoKLLqLPX39VbrvuOnj0UeO2fKd/LuEdOFBH7+zSktQlnP/p+ad8/oWhL1T5B5zOY18u0nZgx48f5/zzz+fzzz9n48aNpKam8vXXX/Pcc89xySWXnPF+7rjjDvbv38+dd97J1q1b+b//+z9mzpzJ9OnTcfrnS6SimPj8888Z+M8XyoABA0hMTGT79u3mNoApU6aYp/q2b9/OG2+8YXN6DeCee+7h448/5u2332bHjh289NJLfPvtt9x9991mm4qi7IcffjD3P3DgQD7//HOaNm1K586dzy64E1Tsr6LoCwgIoHPnznz11Vc2n6mhuO6667BarYwfP56UlBR+/fVXc0b5ii+mRx99lP/7v/9j586dbN68mR9//PFfR8JE3erZs6fuLjiGsjJ4/31jzqKAADixOLriCuPi644dK4sjcVpWZcXjKQ8sj1u4/H+XV1schXiH8OaFb6JmKmacU/X0pc5jX/4vOzAfHx/69OnDyy+/zIABA4iOjuaRRx5h3LhxvPHGG2e8n+bNm/Pzzz+zZs0aunfvzsSJE7n11lt5+OGHbdoNGjSI8vJys3CoKCaaNm1q8wu4b9++vP/++7z++uvExMSwaNGiKvsaPXo0r776Ks8//zxdunThnXfe4aOPPrIpSvz9/enRoweBgYFmMdS/f3+sVmutje6c/JnAGKUqLy9vkCNIfn5+/PDDDyQlJRETE8NDDz3Eo48+CmBel+Tm5sYDDzxAt27dGDBgAM7Oznz44Yc6u+3wEhISdHeh8VIKFi6E224zJnIcN87Y7uICO3eS9NNPUFICX38thdEZsjxuwfK4BecnnCkuN+6Q/jblW67ofAWJ4xOxPmplyU1LOHrPUY7cfYQ7et1xyn3pPPZlqZEacNSlRhqKhrjcRWPwxRdfcPPNN5OdnY2np2e1bRpi9o70d06WGqllWVnw+edw552226Oj4aOP4IRRC8m+ekfyjjDuh3FsP76d+869jyYeTfBx8+GKr68gpzjHpu3XV37Nea3OI9Sn5pPl6lxqRK5BEnZDlruoHZ9++ilt27alefPmbNiwgfvuu4+rrrrqlMURSPa6NW3aVHcXGo+CAuP02cm++w4uvrjKKJFkb0hOTyYqOIq0vDRavtwSJ4sTVmXc/HHL97dUab923FoO5h6khV8LYsPO/mJcnflLgSTshlyoWjvS0tJ49NFHSUtLIywsjCuvvJKnn376tK+R7PWqryVlGrXcXGOh2PvvNx6/8AJMnQqpqRAeDt7e1b7MkbNPOZrC/pz9vPL3K/yy8xeb54a2Hcr4uPE899dzpOen4+bsRqm1lGcGP8MlUZfg5uxGHP99nhud+UuBJOxGUVFRgzvNY4/uvfde7r333hq9RrLXa9u2bXKa50wcPgwXXQSJiZXbrrgCvvnGtt1991XOZ/QvC8U6avaLdi1i+OfDq2yPC4tjZvxMRnU0lnK6rNNlddoPnflLgSSEEMK+LVwIF1xQ/XMnFkcvvgjXXgv/TMoqbG0/vp38knx2Z+7miq+vAODCyAv54rIvaOLRRG/nNJACSdiNxn4hbkMm2evVoUMH3V1omJSCTp2gYpHn7t3hoYfgyiuN5zZuhP37YfBgOM01dqfjKNl/m/Itl//vcptt7416j9tib9PUI4PO/KVAEnajvLwcFxc5ZHWQ7PXKzMwkoLoLix1FSQm4ucHy5cZo0bBhMH++MW/Rtm3G4//7PzixkLdYjILpP85w3dizzyrK4p5F9/D++vfNbd2adWP9hPU4WfRPa6Azf/nGE3ajtLTUnP1Z1C/JXq+jR4/Stm1b3d2of0rBs8/CAw/Ybp81q/LPcXHGch91pLFlr5SisKyQNQfX4OfuR9y7lRdSH7jrQJW10HTTmb8USMJunDwFvag/kr1eDpN/QoKx2GuPHtU/37MnlJYa6zhddBH07g3nn3rpitrQWLKfv2U+S/cs5c99f7LxyEab554Y+ATT+k7D1933FK/WR2f+UiAJu+F9ittwRd2T7PXq3bu37i7UvtJSePJJ4+d0oqNhyRI4eFDLgrD2mr1SihdWvsC9i099x2oLvxZ8OvpTBrUZVI89qxmd+es/wSjsztKlS7FYLGRlZdXqfiMiInjllVdO+Xx+fn6tvp84c5K9XuvWrdPdhdqVmgpt2lRfHN1+Oxw7Bhs2GKfYNm2C4GAtxRHYX/ZKKZ776zmcnnCqUhx9ePGHZNybgZqpUDMV++/a36CLI9Cbv4wgObi3336be+65h8zMTPMi3Ly8PAICAujbty/Lly832y5fvpwBAwawbds2Dh8+jL+/f632JSEh4bQjFbIqjj6SvV5lZWW6u3D2ysuN2akPHoSxY+H33yufGzYMFiyo/g6zoKD66uFpNZTs0/LScHd2J8Cz+guWn1j2BDOXzrTZNvfyuVwTfQ3HCo4R5Blkl6cLdeYvBZKDGzRoEHl5eaxdu5a+ffsCRiEUGhpKQkICBQUFeHl5AcbIUXh4eJ3ddvlvU8rLXVT6SPZ62eVszkrBe+/Bgw/C8eO2z117LTz6qDFJYwOfpV139i+teokZi2xXue/erDsbjmwwH0cGRrIjY4f5eHKvyTw39Dk8XY3CM9gruH46Wwd05i+n2Bxcx44dCQ8PZ+nSpea2pUuXcskll9CuXTtWrlxps33QoEFVTrF9/PHHNGnShF9//ZVOnTrh4+PDiBEjOHz4sPnasWPHMnr0aF544QXCwsIICgpi0qRJlJaWmm1OPsVmsVh4//33ufTSS/Hy8qJr1658//33Nv3//vvviYyMxNPTk0GDBvHJJ5/Y9G3v3r2MGjWKgIAAvL296dKlCz///HPtBeggZC02vZo1a6a7C//uuecgJMS4yPryy8HHByZMqCyO/Pzgjz/AaoUvv4SoqAZfHIGe7A/lHqKorIjr5l9nFkduzm7m8ycWR4BZHG2YuIGc+3N4/cLXzeLI3uk89qVAEgwcOJAlS5aYj5csWcLAgQOJj483t5eUlLBq1SoGDar+fHVBQQEvvPACn332GX/++Sf79u3j7rvvtmmzZMkSdu3axZIlS/jkk0/4+OOP+fjjj0/bt8cff5yrrrqKjRs3MnToUK6//noyMjIA2LNnD1dccQWjR48mKSmJCRMm8NBDD9m8ftKkSRQXF/Pnn3+yadMmnn32WVky4ywUFhbq7oJDS0lJ0d2F6v3+O8ycCYMGGct3HD1qzE307beVi8ImJRmjSdnZRjs7O81T39mXW8tp/lJzPJ/2ZG7yXACO3nOU4oeLUTMV7170Lnf0vAM1U5H/YD6/jfmNXVN2oWYqujXr1iDvRPsvdB77Mm5ehwpKC9h6bGu9v29UcBRerl5n3H7gwIHcddddlJWVUVhYyPr16xkwYADl5eW89tprAPz9998UFhYyaNAg9u3bV2UfpaWlvP3227Rr1w6AyZMn88QTT9i0CQgI4I033sDZ2ZmoqChGjhzJ77//zrhx407Zt7Fjx3LttdcCMHPmTN5++23WrFnDiBEjePvtt+nYsSPPP/88YIyGJScn2yy8um/fPi6//HK6du0K0KjmMxGiXv32G1xyiXHd0M8/Q+vWsHNn5fNt28IXX0BmpjGD9bRpIHNnnRGrsrI/ez8zFs1gfsp8c/uzQ57l7nPutpmwcVxc5fell6sXQ9oOqde+OhIpkOrQ1mNbbSbhqi/rxq8jNiz2jNsPGjSI/Px8EhISyMzMpEOHDoSEhBAfH8+YMWPIz89n6dKltGrVirZt21ZbIHl5eZnFEUBYWBjp6ek2bbp06WKzKnxYWBibNm06bd+6detm/jkoKAhfX19zv9u2baNXr1427U++JXTKlCncfvvtLFq0iCFDhnD55Zfb7FOcGVlqRK/27dvr7UBhIYwebfz3//7P2FZRHHl6wquvwon/0DnVumh2qK6zLygtIPzFcLKLs81tr414jcm9J9vlRdW1TeexLwVSHYoKjmLd+Pq/RTEqOKpG7du3b0+LFi1YsmQJmZmZxMfHAxAaGkqbNm3466+/WLJkCeefZkK2k69RsVgsVe58qq6N1Wo9bd9OfE15ebnNa5RSVb5ATn7P2267jeHDh/PTTz+xaNEiZs+ezYsvvsidd9552vcVtmSpEb3y8vIIquu7ug4eNJbxeP11yM2Fbt3gmWeM02ivv26cMvvuO+Mao9BQSEw0rjWyxwvIa6Aus1+2ZxkDPxkIwOio0UyMm0jfFn3x96jdO4TtWb0c+6cg33h1yMvVq0YjOTpVXHydmZnJPffcY26Pj4/n119/5e+//+bmm2/W2ENsLugGiIqKqnLB9dq1a6u8rmXLlkycOJGJEyfywAMP8N5770mBVEOy1IheaWlptG7d+r/vKD8f9uwxbrsHaNUK1q2Dm2+G3btt2+7ebdyCX2HuXGMUqUJc/Y+O61Br2f+j3FpOwqEE/tr3F3f/Zlyn+cB5DzBr8Kx/eaVjqu38a0IKJAEYBVLFXWUVI0hgFEi33347RUVFp7xAW5cJEybw0ksvcd9993HrrbeSlJRkXvRdMbI0bdo0LrjgAjp06EBmZiZ//PEHnTp10thrITTYtQs++ABmzz59u6uvNu48O/dcmDQJunSB4mK4557Kokqcsb1Ze7lo7kU8P/R5SspL+GHbDzaLwgKkTEqp8ai/qB9SIAnAKJAKCwuJioqyua0yPj6e3Nxc2rVrR8uWLTX2sOpyF23atOGbb75hxowZvPrqq/Tr14+HHnqI22+/3RztKC8vZ9KkSRw4cAA/Pz9GjBjByy+/rKP7dk2WGtHrjJdbOHTIOPV10UXG4wUL4NJLK58PCIBzzoGffgJ/f2jZ0ji19tJLxiSOJ3rvvdrout2rLvusoixWH1hNx+COBHsF4+PmQ7m1nPVp6/ku5Tuu63odL616iQ+TPgTggi8qr8nq3bw33q7eHMw9SMqkFJsLsEVVOpcasSiZIveM5eTk4O/vT3Z2Nn5+fjbPFRUVkZqaSps2beSC1jpy4qSVp/L000/z9ttvs3///nrqlWM4k+zrmyP9nVu/fj09evSArCzjDrIOHWD6dPD1NR7/mxEj4MMPISyszvva2FRkb1VWzvvwPFYdWFWlzYvDXqwymWOFiztezNH8o5zX6jym9plKc7/mdd3lRsU89v+j0/3+PhUZQRJ2o7oLut966y169epFUFAQf/31F88//zyTJ0/W0LvG7d8uphd1q6SkxJhb6PLLT9/Q3d04JXaioiK53f4sFZYWcjTvKNfNv86ck6iCr5svuSW5AGZx5Gxx5qsrvuKxZY/x0rCXGNpuaL33ubEpKSnR9t5SIAm7Ud1dVDt27OCpp54iIyODVq1aMWPGDB544AENvWvc5A42vZr4+1cWR/37G7fY//orJCfDZZcZM1Kf+P8oMxOOHDFOoUlxVCPrDq1jyGdDyCrKqvLcuxe9yzXR1+Dj5oPFYqGkvITXVr/G/uz9DG03lAsjL8TJ4sTlnf+lkBVnrEmTJtreW06x1YCcYtOrvLzcZh4lUX8aYvYO83cuLY3ya67BedkyYw6iiy/W3aNG53DuYRIOJfDK36+wZM+SKs+H+YRx/3n3M6XPFA29c2z5+fm1cg2knGITjVphYaEsE6KJZK9BSQn07QtbtuBcXAyffirFUS3ak7WHG769gaKyItYdNuar83Tx5NURr9IxqCOhPqFEh0SzNmEtffr00dxbx5WcnKwtfymQhBCiocnPNxZzPXAAhg8ncdIkYkeN0t2rRkEpxbXzr+WrzV/ZbB8fO55nhjxDgGeApp6JhkYKpFomZyzrjkxUqE9DzL5R/l377Td4663KCRofeQSeeIKWR49q7Za9yCvJ477f7qNz085M6DkBZ4uzOSfain0r+Hrz16w+uJrVB1cDkDAugQ8SP2BCzwnEhMZUu09Zv1EvnflLgVRLKq7PKCkpwdPTU3NvGqdG+QvRTjTE7AsKCoCqS9jYndxceP55eOcdOHH9wtdeg39mfC8qKtLUOfuwaNcihn8+3Gbb5F+Mu1kfH/g4H67/kL3Ze83n2ge2Z/Mdm3FzdqNneM/T7luy10tn/lIg1RIXFxe8vLw4evQorq6uOMmss7Wuti7WEzXXkLJXSlFQUEB6ejpNmjRpcBeP14jVCoMHQ0JC5bavvzbuWDthncFDhw5pn6i1obr9x9t5e93b5uNro6/laMFRFu9eDMDMpTPN5x6Lf4wWfi24KeYmXJzO7NefZK+XzvylQKolFouFsLAwUlNT2bt377+/QNRYcXFxgzzV4wgaYvZNmjQhNDRUdzdqTimYM8dYyqPC44/Do4/q65MmhaWFHMk/whtr3uBI/hG6hXTj9l63823KtzT3bc7gtoPNtsnpyXSd0xWAtBlpuDi5sHzfcrM4SpqQRPfQ7mb7cms5i3cvZlP6JqzKyl1978LV2c5HG0W9ktv8a+BMbhO0Wq1aJ7ZqzMrKymQ+Hk0aWvaurq72OXJUWgrXXGNM+nii3Fw4zV2CDS3/2rDl6Bb6vt/XnGzxVCxYUJz+11TJwyV1Vvw0xuztSW3lL7f5NwBOTk6Ne04WjTZs2ED37t3/vaGodZL9WSgqMiZwdHU1Ro3eeQduv9147o474M03ITUVvL1PWxwBbN68uVHl//nGzxnz3RgABkYMZGjboWw7vo1PN3wKQLBXMMcKjgHYFEepU1O5/tvrWbl/JQB39b2Le865p05Hhhpb9vZGZ/5SIAm7IRdL6iPZ19DChXDBBdU/t3ixcd0RQJs2Z7Q7e86/3FqOk8WJkvISftrxE7d9fxuZRZkAfH3l11zR+Qqz7SMDHiHhYALXdr0WpRTr09azYOsCcotz8XHzIaJJBH/d8le99t+es28M5CJtIc7AmQ6Liton2dfAc8/BffdV3X7BBfDTTzYXX58pe8nfqqwUlhbi7eaNUooP1n/AtIXTyC/Nt2kXGRjJ55d9Tu/mtiu1tw9sT/vA9oBxXWdsWCyxYbH11v/q2Ev2jZXO/KVAEnYjIiJCdxcclmR/BnJzjbvPfvvNeLxtG7RvDz/8AMOHw3849d7Q8y8tL+WcD89h7aG1AFzd5Wp+2vETeSV5Nu0m9ZrEHb3uoHPTzjq6eVYaevaNnc785V50YTc2btyouwsOS7I/jWPHjFGj1q2N4ig+3pgBu0MHcHKCSy75T8URNJz8y6xlgDHVglKK8T+Mx/K4Bben3MziCOCrzV+RV5KHv7s/6yesR81UqJmKNy58w66KI2g42TsqnfnLCJIQQtRUaiqcPMOvpyf8739w5ZX12pXtx7fT2r817i51Ow3DFxu/4Ibvbjjl8/efez+zBs/il52/sP7wemacMwMPF7lhRdgvKZCE3WjdurXuLjgsh86+pMRYKHblSujZ07iG6I47bNtYLMZkj1261EkXTs5fKcWfe/9kzto55ppiP1/3M8v3LWde8jxSs1Jp6tWUhTcstLmGJ7som082fEL3Zt1pF9iOFn4tzuj9S8tLmbFoRrXPfX/N97Tyb2XOQXRh5IVcGHnh2XzMBsmhj/0GQGf+UiAJu1FeXq67Cw7LIbMvK4O77oI33qjc9tFHxn8vuggeesi4Cy042Lidvw6Vl5ez9dhWOr3ZCQ8XD2JCY/j7wN82bS780rYoOVpwlLh34yh9pBSrsvK/zf8zb62vMKbbGG7veTsLti5gZIeRfL7xc95LfA8XJxcGtB7A4jGL2ZW5iwk/TuBI/hFW3bqKdgHtCPQMpFyV4+bsVqefuyFwyGO/AdGZvxRIwm4cOHCA5s2b6+6GQ3Ko7OfMMe42+/13Yy4jgLlzYdgwYy6jPXuMNvW4nNCBAwe4dd2tABSVFZnF0ZWdr+Tj0R/zwOIHWLp3KU29mvJ76u82r30r4S2mLpxqPr6448VsSNvA3uy9fLbxMz7b+BkAz618zmxTZi3jj9Q/cHqi8jO+OOxF+rboaz52xg4n6jwLDnXsN0A685cCSQghAPLzoW9fSE6u3PZ//2dcdO3vbzx+4IF671aZtYz3d77P5qObuazTZcyMn8ncTXN5fNDj5gjOqxe8arZPSkuie7Pu/LLzF0Z+OdIsjq7qchXvj3ofX3dfwJjJ+vafbufvA39TUl5CiHcI955zL+PjxuPs5MwFX1xAp+BO7MvexxeXfUGAZ0C9f3YhdJKlRmrgbKYqF7WnpKQEN7fGP6TfEDXq7I8ehdGjjWuMwDht9uOP0LFjnZ86O50yaxktXmrBkfwjAIyLHcebF755xrNG/7rzV0Z8MQKAY/ccI8grqM762pg16mPfDtRW/mfz+1tu8xd2Y/v27bq74LAaTfYV/x5MSzPmJ3riCQgJqSyO/vc/2L0bOnfWWhyVlpfS9/2+ZnH0Xv/3eHfUuzVaUqNfy348OuBRsu/PluLoP2g0x76d0pm/nGITdiM/P//fG4k6YdfZl5bCxInw4YfVPx8Zadya//TT9duvahzJO0JaXhox78QAxlpjLw1/idWrV9d4X37ufjw+6PFa7qHjsetjvxHQmb8USMJu+PzLgp6i7tht9sXF0K4dHDxou71bN+Mi6/vug2uu0dO3EyiluP7b65mbPNdm+0vDXwLsOP9GQLLXS2f+UiAJu9G+fXvdXXBYdpV9bq4xWhQXBw8/bBRHV1xhnD47i3XQ6sOwz4exePdi8/H42PHMGjzLfGxX+Tcykr1eOvOXa5CE3UhKStLdBYdlN9k/+ST4+cG0adC/PyxbBlOmwNdfN9ji6OcdP7N492ICPQPNJTneGfWOzXVDdpN/IyTZ66UzfxlBEkLYp6wsCDjFrefvvWeMIk2fboweNUB7svbwR+of3Pq9Mb/Rlju2aO6REOJEUiAJu9GyZUvdXXBYDSr7khIYORIWL7bd3qWLMWfRc8+Btzfcdpue/v0LpRQP/v4gz/z1jLktYVwCzXyanfI1DSp/ByPZ66UzfymQhN1wqseZi4Ut7dkrBffeCy+8AC1bwv79xvYXXzSWA2mgp8+qM+a7MXyx6QvAuNPs+q7X0zO852lfoz1/BybZ66UzfymQhN3Yu3cvoaGhurvhkLRm/9VXtneatWtnFEpXXVXrb7U3ay+erp6EeIfU+r4BXlz5Il9s+oLokGg23b7pzPslx742kr1eOvOXAkkI0TB9+CHcemvl4zvuMBaOraPRIqUUEa9G0CGoA9smb/tP+yoqK8LzaU8ArI9aKSwrpN8H/dh4ZCNXd7maDy7+oDa6LISoQ1IgCbvRrVs33V1wWPWe/eTJ8Oabxp9fesm4K60OT6NZlZW3Et4CYGfGzv+0rw1pG8yJHgGbBV9v73k7b1z4Bk6Wmp02kGNfH8leL535y8lVYTdSU1N1d8Fh1Vv2hYXGdUUVxVFhYa1dY1RQWsCGtA2UlJfYbH951cs4P+HMnb/cCRjF0rGCYzXe/5G8I0S/FW0WR29e+CaDIgaZz18adSlvjXyrxsURyLGvk2Svl878ZQRJ2I3c3FzdXXBYtZp9VhbMnw/XXw8eHsa2P/805ivasMF47OsLf/xR+fx/MH/LfK742vZW//NanceKfStO+Zrk9GQGRgw8o/2n5aUxc8lMvkz+krySPLxdvfnl+l/o37o/d/S6g9ziXDIKM2jdpPVZfwY59vWR7PXSmb8USMJueHp66u6Cw6qV7JOSjNvzDx0yHld3G76bGyxfDr17n9EurcpKfkk+R/KP0D6wPUfzj9LutXbkluTycP+Hubzz5WZxZMFCbFgs6w6vsymOxsWO45EBj9DSv6V5eszV6d8XhX3kj0d4avlT5uMAjwAW3bCIoe2G2rTzdffF1933jD7Pqcixr49kr5fO/C1KVSxvLf5NTk4O/v7+ZGdn4+fnp7s7Dqe0tBRX1zNfzVzUnv+UfUEBXHYZ/Pqr8TgoCI4ft23Tti0sXWrcwn+Gft35KyO+GGE+jg2LJfFwYrVtd965k3aB7QBjgsYQ7xDWHlpLXFgc3m7eZrsdx3fQ4Y0OLLlpiTmC9Pvu3/lg/QcUlhUytc9U2jRpQ98P+pKWl3bK96htcuzrI9nrVVv5n83v7zq9Bmn27Nn06tULX19fQkJCGD16NNu22d4dopTiscceIzw8HE9PTwYOHMjmzZtt2hQXF3PnnXcSHByMt7c3F198MQcOHLBpk5mZyZgxY/D398ff358xY8aQlZVl02bfvn2MGjUKb29vgoODmTJlCiUlttcjiIYrMbH6X36i7tU4e6Xgk09gwABj0sZffzVuz9+/H44dg8OHjeU/srONtrt2nXFxVFhaSM93e9oUR4BZHL064lXyH6xcATxhXIJN4RLRJAIvVy8GtB5gUxwBuDobX8TZRdkkpSVhedzCkM+GMDd5Lgu2LmDQJ4OIeDXCLI7+uPEP8h7IQ81UdVYcgRz7Okn2eunMv05PsS1btoxJkybRq1cvysrKeOihhxg2bBhbtmzB29v4Ynruued46aWX+Pjjj+nQoQNPPfUUQ4cOZdu2bfj6GsPS06ZN44cffmDevHkEBQUxY8YMLrroItatW4ezszMA1113HQcOHGDhwoUAjB8/njFjxvDDDz8AUF5ezsiRI2natCkrVqzg+PHj3HTTTSileP311+syBiEcw913GxdYV+e992xPqYWG1mgJEKuyMv6H8Xyw/gOaeDQhqyiLIM8gfr7+Z3o3782CrQvIL8nnmuhrcHYyvhPUzJoPjrs5uwEw+qvRNtvXjV+Hm7MbXed0BWDFzSs4t9W5Nd6/EMKOqHqUnp6uALVs2TKllFJWq1WFhoaqZ555xmxTVFSk/P391dtvv62UUiorK0u5urqqefPmmW0OHjyonJyc1MKFC5VSSm3ZskUB6u+//zbbrFq1SgFq69atSimlfv75Z+Xk5KQOHjxotpk7d65yd3dX2dnZZ9T/7OxsBZxxe1G79u/fr7sLDutfs587VyljLKjyp2lTpWbOVOrw4f/03u+sfUfxGObP6Hmj1S87fvlP+zwVq9WqPJ7yUDyGCn8xXKVmptbJ+9SUHPv6SPZ61Vb+Z/P7u14v0s7OzgYgMDAQMG7fS0tLY9iwYWYbd3d34uPjWblyJRMmTGDdunWUlpbatAkPDyc6OpqVK1cyfPhwVq1ahb+/P3369DHb9O3bF39/f1auXEnHjh1ZtWoV0dHRhIeHm22GDx9OcXEx69atY9CgyttxKxQXF1NcXGw+zsnJqb0wRI25ubnp7oLDqjb7jRuNW/D/+KNyW16e8WOxQMjZz0b994G/6fdBP5ttT5//NA/2f/Cs93kmLBYLhQ8V1ul7nA059vWR7PXSmX+9FUhKKaZPn855551HdHQ0AGlpxnn8Zs1sF2ls1qwZe/fuNdu4ubkRcNKq3c2aNTNfn5aWRkg1X8YhISE2bU5+n4CAANzc3Mw2J5s9ezaPP/54le1r167F29ub2NhYUlJSKCwsxNfXlzZt2rBx40YAWrdujdVqZf8/a0bFxMSwc+dO8vLy8Pb2pkOHDqxfvx6AFi1a4OzsbH7mbt26sWfPHnJycvDw8KBLly6sW7cOMIpDDw8Pdu/eDUB0dDQHDhwgKysLNzc3YmJiWLNmDQChoaH4+Piwc6cx8V2nTp04cuQIGRkZuLi4EBcXx5o1a1BK0bRpUwICAti+fTsAHTt2JCMjg6NHj+Lk5ESvXr1Yu3Yt5eXlBAUFERISQkpKCgCRkZHk5ORw5MgRAPr06UNiYiKlpaUEBAQQHh5uXlfWrl07CgoKOHz4MAA9e/YkOTmZoqIi/P39adWqFZs2GUswREREUFZWZl5vZrVaOXLkCAUFBfj4+NCuXTs2/HNbeKtWrQDjOjOA7t27s2vXLvLy8vDy8iIqKso8l92iRQtcXFzYs2cPAF27dmXfvn1kZ2fj4eFBdHQ0a9euBSAsLAwvLy927doFQJcuXTh06BCZmZm4uroSGxvL6tWrAeOY9PPzY8eOHWbe6enpHD9+HGdnZ3r27ElCQgJWq5WmTZsSGBhoXpPXoUMHMjMzOXr0KBaLhd69e7Nu3TrKysoIDAykWbNmZt7t27cnLy/PPG579+5NUlISJSUlNGnShBYtWpCcnAxA27ZtKSoq4tA/d47FxcWxefNmioqK8PPzIyIiwuaYLS8vN/Pu0aMH27dvJz8/n5ycHAYMGEDS+vUELF1K5GOPYfnnHw/ZPXviedNN7DzvPHKTk/H09KRTp04k/pNL8+bNcXNzM+cz6dq1K/v37ycrKwt3d3e6detGQkKCecwqV2VTHD3b91kG+A8wvygr8g4JCcHf39/MOyoqimPHjnHs2DHzmK3IOzg4mODgYLZu3Woes9nZ2aSnp1c5ZgMDAwkNDWXLli3mMZufn2/m3atXLzZu3EhxcTFNmjShZcuW5jHbpk0bSkpKOHjwIECtfUcUFRVhtVrlO+JfviNiY2PZunVrrX5HZGZmMmDAAPmO4PTfET4+PrRv356kpCTAWGTWycnJ5phNTU0lNze38jvin7xP9x2RnZ3NkCFDbL4jvL29zbw7d+5MWloaGRkZVfI+8TsiP7/yusQzVitjV2fgjjvuUK1bt7YZLvvrr78UoA4dOmTT9rbbblPDhw9XSin1xRdfKDc3tyr7GzJkiJowYYJSSqmnn35adejQoUqb9u3bq9mzZyullBo3bpwaNmxYlTaurq5q7ty51fa5qKhIZWdnmz/79++XU2wanXgKVdSjhQtV+oUXKjV0qO0ptNBQpdLSarSr9YfXq992/aYeWPyAKi4rtnlu05FNqtMbncxTaa+seqU2P4Vdk2NfH8ler9rKv8GeYrvzzjv5/vvv+fPPP2nRooW5vWIBurS0NMLCwszt6enp5mhPaGgoJSUlZGZm2owipaenc84555htKv5lcqKjR4/a7KeiqqyQmZlJaWlplZGlCu7u7ri7u5/NRxZ1oGvXrrq74DjS0mDWLPjnBoamFdstFmjf3ji9VsNJHNccXEOf9ytPg89eMZs+zfsQ7BXMn3v/JLekckK4dy56h5tjbv6vn6LRkGNfH8leL5351+lt/kopJk+ezLfffssff/xBmzZtbJ5v06YNoaGh/Pbbb+a2kpISli1bZhY/cXFxuLq62rQ5fPgwycnJZpt+/fqRnZ1tDhuDMQyfnZ1t0yY5OdkctgVYtGgR7u7uxMXF1f6HF7Wu4lSEqGO7d0PXrmZxBHDgrbdg4UIoL4ft22tUHBWUFjD0s6FmcXRn7zvN51YfXM1PO34itySX3s17s/zm5aiZivFx481b7oUc+zpJ9nrpzL9OR5AmTZrEl19+yf/93//h6+trng/19/fH09MTi8XCtGnTmDVrFpGRkURGRjJr1iy8vLy47rrrzLa33norM2bMICgoiMDAQO6++266du3KkCFDAOM87ogRIxg3bhzvvPMOYNzmf9FFF9GxY0cAhg0bRufOnRkzZgzPP/88GRkZ3H333YwbN04mfbQTJ89rJWpRTg7MmwcTJlRu++sv43b8Fi04uH49LU64CaI6xwuOk5SWxOC2g1FK8eH6D3ln3TskHDKuHegQ1IE/bvyD5n7Nuffce1m8ezGrD6zmvFbnERMaQ5eQLnX5Ce2aHPv6SPZ6ac2/Vk7unQJQ7c9HH31ktrFarWrmzJkqNDRUubu7qwEDBqhNmzbZ7KewsFBNnjxZBQYGKk9PT3XRRRepffv22bQ5fvy4uv7665Wvr6/y9fVV119/vcrMzLRps3fvXjVy5Ejl6empAgMD1eTJk1VRUdEZfx65zV+v9evX6+5C42O1KtW/f9Vb9L/7zqbZv2VvtVqVzywfm9vxT/z5MPHDuvsMDkCOfX0ke71qK/+z+f0tS43UgCw1opfVasXJqU7PCjdOhYXw44/wyCOwebPx+M03YfFiY82zWbOMdtOnw7PPgkvVgeXTZX+84DhPLHuC19a8ZrM9zCeMSb0mMS5uHCHeZ3/Lv5BjXyfJXq/ayv9sfn/LYrXCbiQkJNjMdSVOYft2eOstyMyEzz4zxoQqnFz8LF4M559v/NdiOeUuT84+uyibb1O+Zdqv08gpNuYHGxgxkD9u/AOForisGE9XWeSztsixr49kr5fO/KVAEqKx2LkTIiNtt7m6GtuuvhpmzjS2RUbCsGEwbRq89ho88cRpiyMwbriocO9v9/L8yufNx/edex9XdbmK2LBYACxYpDgSQtg9KZCE3aiYFsLhlZbCTz/BpZdCYKAxWvT00/DP5HkAfPst9OxpuwDso49W3ddrr1XdBhzMOcgH6z+gR2gPLp53MQAjdo1g4U5jrUM3Zzcu6XgJn176KR4uNbvdX9ScHPv6SPZ66cxfCiRhNyoWOHZoSUlG4VNebjzOyIBrrjH+HBYGH30EMTFwirm9zkRpeSlDPxtKyrEUm+0VxVG/Fv1YfvNyc1FYUffk2NdHstdLZ/5SIAm7sWvXLoKDg3V3Q49jx4wRoDlzKrd9/rkxalRWZpwm8/I6692XlpeyP2c/7V5rZ277ZPQn3PXrXXx48YeoNEVWkyxu7H4jTha5YLW+OfSxr5lkr5fO/KVAEqIhS0yEF1+EL780HrduDb/8Ap061dpbJKUl0eOdHjbb3rzwTW7sfiM3dr8RgNXZqxkdM7rW3lMIIRo6KZCE3ejcubPuLtSP8nJISYG774Zff63c/uuvxsXVNXSs4BjPrHiGF1e9SNZ9WRzOO0xucS5NPJrgZHGyKY5W3rKS6JBofN19bfbhMNk3UJK/PpK9XjrzlwJJ2I20tDR8fX3/vaG9KSiABx80RomOHq36/IQJ8MorNV77DGD458NZtGuR+bjJs02qbffDtT9wUYeLTrmfRpu9nZD89ZHs9dKZvxRIwm5kZGTo7kLtKC+H/HyIjobq1hkaNQp++MG4Df+554xb9c+QUorvt33P5F8m42RxYl/2PgDeH/U+32//nu+3fV/lNZN6TTptcQSNKHs7JfnrI9nrpTN/KZCE3XCtQaHQYBQWwurVMGkS5OZWXxBFR8PYsdCnD3TsCE2b/uturcrKw388TPvA9tzS4xaUUjz0x0PMXjHbpt2jAx7lsYGPYbFYuDX21ir7WbFvBee2PPdf388us29EJH99JHu9dOYvS43UgCw1Imrkyy/h+uurfy48HJYuhfbt/3WSxpM9/MfDPL38afNxc9/mHMw9aNMm875MPF08cXdxr2mvhRCi0Tmb399yv66wG6tXr9bdhTOnlDFqBHDOOcYaaGvXgtVqPHfwoDGjdQ2Ko1X7VxH0XJBNcQRwMPcgId4hTOo1ia+u+Iotd2yhiUeTWi2O7Cr7Rkjy10ey10tn/nKKTYjatm8fXHABZGXBn39C//5nvauisiK2H9/OlF+msGzvMgA6BHVgw8QNeLh4sPbQWpp4NKF9YPta6rwQQgiQAknYkZAQO1gRfts2iIoy/vzss2ZxVFJeQpm1jPT8dL7c9CXJ6cnMTZ4LwOA2g9mVuYv5V82npV9L7vntHgI9A3n575dtdn1LzC28POJl/Nwrh4d7hvesl49lF9k3YpK/PpK9XjrzlwJJ2A1/f3/dXTi9/PzKZT/274cWLViwdQFTF0417yarzu+pvwMQ927cKdssvWkp8RHxtdrdmmjw2Tdykr8+kr1eOvOXa5CE3dixY4fuLpzaW2+Bj4+xVtr69dCiBYM/HcylX11qUxx5u3oTFxbHT9f9RPmj5cy/aj6J4xPp3by32Wbe5fMY020M7170LsfvPY6aqbQWR9DAs3cAkr8+kr1eOvOXESQh/ovycuNOta++Mh4vWQIxMZSWl/JH6h8MbjOYDy/5kFb+rap9+WWdLgNg9W22FyJeHX11nXZbCCHE6ckIkrAbURXX9tS3tDRjrqIXXjDuQANjkdgmTcDFpbI42rYNBg4EYPm+5QBM6TPllMWRPdGWvQAkf50ke7105i8FkrAbx44dq/833bYN4uON2/TvuQecnIxb87t1g+zsynbl5dChAwDP/fUcgz8dDMDAiIH13+c6oCV7YZL89ZHs9dKZvxRIwm7U61+Uo0eNeYqioiAvDz791Pb5YcOMa41KSoziyMn4q/Tphk+5b/F9xIbFsmfqHps7zuyZ/JLQS/LXR7LXS2f+cg2SsBtOTvVUz+/ZA23aGH/u2dOYETsyEi69FFJToWvXKi9RSjFz6Uye/PNJYkJjWDtuLZYazpDdkNVb9qJakr8+kr1eOvOXpUZqQJYaaeTKymD8ePjoI+PxPfcYi8X+i0O5h2j7aluKy4uJCY1h1a2r8HDxqOPOCiGEOFOy1Iho1BISEupu5+XlEBdXWRwdOmQWR5mFmQz5dAjfpnxb5WWFpYV0ndOV4vJigr2CG21xVKfZi38l+esj2eulM385xSbshtVqrZsdp6RAly7GHWo33GBcb2SxUFpeyrGCY4S/FA5UTujo6+ZLbkkuYT5hHM47DBizXL9/8fuN6rTaieose3FGJH99JHu9dOYvI0jCbgQHB9f+TmfPhs6djeLovffgs8/AYuGJZU/g9pSbWRzN6DfDfEluSS6AWRwBjbo4gjrKXpwxyV8fyV4vnfnLCJKwG7X+F+V//4MHHzT+vHSpcTs/8M2Wb5i5dCYAbZq0YcE1C+jWrBuDIgaxdM9Snjz/SXZm7KSZdzOCvYw+NebiCOSXhG6Svz6SvV4685cRJGE3tm7d+t93ohTMm2csInv11RAaCoWFEB/PwZyDWB63cOXXV9IhqAMlD5ewe+puujXrBsDIDiN5ftjzeLh4EB0STVPvplgslkZfHEEtZS/OmuSvj2Svl878ZQRJOIZffoE77jBu4a/Qti2sWgUeHpSUl3D+p+ebTy2/eTmuzq71308hhBANgowgCbsRGRlZ8xft2wdXXgkXXmgUR87Oxq38u3fDrl0QEkJSWhLuT7mz/fh2Prz4Q9RMRYh3SK33356dVfai1kj++kj2eunMXwokYTeyT1za43QWLABPT2NJkNat4Ztv4NFHISfHmOvonXfMiSAf+eMRerzTA4D/XfE/bu5xcx313r6dcfaiTkj++kj2eunMXwokYTfS09P/vdHzzxszXhcVQbNmxsjR7t3w+OPg62vT9Jcdv/DU8qcASJqQxJVdrqyLbjcKZ5S9qDOSvz6SvV4685drkETj8e23cO+94OEBGRnGKFI1yqxlrNy/kkvmXQJA8cPFuDm71WdPhRBCNHCy1EgNyFIjDYjVatyR5uxsPF6xwrgzDeDYMQgKsmm+5egWft35K9MXTbfZvvKWlfRr2a8+eiyEEEITWWpENGpbvvkGhg41ri1ydQUXF+PPFotRHAUGwl9/QVAQ6w+vJ68kj6P5R7lpwU10eauLTXH0zOBnODzjsBRHZygxMVF3Fxya5K+PZK+XzvzlFJto2HJyjIuq772Xzidur5h+3tnZWEftootg/nz+Tk+k3+NV5yW6ovMVdA3pyr3n3tso10qra6Wlpbq74NAkf30ke7105i8Fkmh4ioshMRGuugoOHDA3l/v54fzXXxAdbRRFa9dCnz7m81uPbaXfB1VHhH6/8XfOb3N+le3izAUGBurugkOT/PWR7PXSmb8USKJhWLAAbr4ZXnoJJk6EkpLK5+bPh0suoaCggDzyeP7X6RSVFRHkGcSwvSXEhcfx++7fuXjexQDMu3weV3S+gnJVLhdf15LQ0FDdXXBokr8+kr1eOvOXi7RrQC7SrgOJiXDxxXDwYJWn1J49vHP0F57961n2ZO05o929MvwVpvadWsudFKtXr6bPCaN1on5J/vpI9nrVVv5n8/tbRpBE/UtOhrQ0ePll+PlnY9usWRwbMYCgux/hw0dG8Unqdyz/OKLKS2PDYpnSewrtAtvR/6P+9Gneh9UHVzOg9QC+uuIrQn3kX3tCCCH+OymQRN3buxcWLoQtW4z/bt9u+3xmJg+sfZZnvj8PBgDLlphPhXiH8OVlXxITGoMqUDYrO6uZMvhZX9q1a6e7Cw5N8tdHstdLZ/5SIInaV1ICU6ZAQoJxMfWGDbbPx8dD377QsSNfts3jrvc7kp5fOVvqrT1upYlHE2YNnmVzDdHeo3ttCiRRf/Lz8yV7jSR/fSR7vXTmLwWSqB1KsXXOk3Q8bqHgyUcJvheKLoYDL8Lkq2FDlyDO946mRddzCQ9sTVpeGs/+NZmCfQUATO0zlccHPo6bsxuertXPgJ2Wlkbr1q3r81OJf0j2ekn++kj2eunMXwokUWNKKRas+YTLFhoLu1oUqBOnHnqo8o8tZvzzB+txPshdBiuX2ezLyeLE/rv2E+4bXredFkIIIWpA7mKrAbmLDXKyjtD+mXCOelrNba7lUOps2+7B8x4kzDeMH7f/yJC2Q7i448Us3r2YOWvncG30tcS3jsfX3Ze2AW3xcfM5o/e2Wq04Ocnk7zpI9npJ/vpI9nrVVv5n8/tbCqQacOgCqbwcxo8nyutDtgVDTKYHn7hdRfTvm3D66WcIDgYXF5RSWCxVZ7KuDUlJScTExNTJvsXpSfZ6Sf76SPZ61Vb+cpu/qH1btsCiRai77uLBwbCtPzwRfBWPzPyq2uZ1VRwBFBcX19m+xelJ9npJ/vpI9nrpzF8KJFGVUsZcRSNGwKFDHPeE4Mcqn75nwidautWkSRMt7yske90kf30ke7105i8nVoVh3Tq4/nqwWMDJCbp1o+TIIdpOheD7jCaDIgZR+kiptsVeW7ZsqeV9hWSvm+Svj2Svl878pUByZGlpcNddRlHUsyd8+SUA6ed0Z/SNrrg/AqkBRtPFYxbzx01/4OKkb9Bx06ZN2t7b0Un2ekn++kj2eunMX06xORKl4J134PbbbTbP7wRXXH3ilsqJHS/vdDlfXfEVzk4n3aYmhBBCNGJSIDVm//sfeHhAt25w663wxx/mUwf84PHRTXi/bVaVl03vO53IoEiGth1Ku8CGM81+mzZtdHfBYUn2ekn++kj2eunMXwqkxqawEJ55Bp54ospTCvjf+9O45sAr/2zJAozJGteNX0dMaEw9dfLslJSU6O6Cw5Ls9ZL89ZHs9dKZv1yD1Jh8+SW0bcumt59gUTuwPAa9xsHe1v4Mf7UXTo9xQnEEb134FmqmovzR8gZfHAEcPHhQdxcclmSvl+Svj2Svl878ZQSpsTh2jB13Xs8Tgyx83rVy89rmEHFzNmQmAPBw/4eJDYtlcNvB+Lk72GSXQgghxBmSmbRroMHOpD1/PurGMbSaUMgBf+gU3InCskLGxY7joT8ewtnizK83/Eq/lv3wcvXS3duzVlpaiqurq+5uOCTJXi/JXx/JXq/ayv9sfn/LKTZ7tmcP9OvH1olX4HWXURx9dMlHbJm0hdSpqTzY/0HUTEXZo2UMbjvYrosjgJSUFN1dcFiSvV6Svz6SvV4685dTbHaovLiIIxNvYOGG+dx6SeX2XuG9uKn7Tfo6VscKCwt1d8FhSfZ6Sf76SPZ66cxfCiR78Omnxi36nTvD5s309/+aVRGFEFHZJDYsltW3ra7TtdB08/X11d0FhyXZ6yX56yPZ66UzfymQGrLjx8loGczmpjC3K6xMhV3NIc/dePr+c+9n1uBZ7M3eSyv/Vo26OAKZj0QnyV4vyV8fyV4vnfnLNUgNidUK995rLP1hsbCoTzBB98GAW2BOL9gQWlkcrRu/jtlDZmOxWIhoEoGTpfH/r9y4caPuLjgsyV4vyV8fyV4vnfnLCFJD8eGHFI+/FbdyOOYFIfdWPhUdEs1D/R+iQ1AHCksLiQyKJMQ7RF9fhRBCiEZOCiTd8vPh1VcZ//dDvPeI7VOXRl3KR5d8hL+Hv56+NTCtW7fW3QWHJdnrJfnrI9nrpTN/KZB0ycrCesEIJjZdzXtxQJzt048OeJTHBz2upWsNldVq1d0FhyXZ6yX56yPZ66UzfymQ6tObb8L8+dC+Pbs2Lydm0FbzmiKArPuyKCkvoal3U319bMD2799PeHi47m44JMleL8lfH8leL535S4FUH0pK2HjnVXQP/z/G+sGD85fw2EDjgutZ588iJjSG4e2HO8SF1kIIIYQ9kKVGauBspipXu3fz4H1xPBOdVeW5WefP4oH+D9RyLxuv4uJi3N3d/72hqHWSvV6Svz6SvV61lf/Z/P6WEaRaZlVWLnxnAL8e+atyYzT44M7/rvuOL5O/pKS8hGDPYO4/7359HbVDO3fupEuXLrq74ZAke70kf30ke7105u9w53Teeust2rRpg4eHB3FxcSxfvvys95W6dwOWxy1MfjSOwuJ85i56idCH3W2LIyDKrTkZD+dyQeQFfHbpZ3x1xVe8OfLNRj+xY23Ly8vT3QWHJdnrJfnrI9nrpTN/hyqQvvrqK6ZNm8ZDDz3E+vXr6d+/PxdccAH79u2r8b72bk+g7ccxALzpnIjXMz5ct2oGR93KeG9nJ5I+9WZBwB2kzzjClvv34+osq0H/V97e3rq74LAke70kf30ke7105u9Q1yD16dOH2NhY5syZY27r1KkTo0ePZvbs2f/6+opzmPu//IiW228GYHHQXQw5/jIAN+704fFbPyNi4Og66b+jKykpwc3NTXc3HJJkr5fkr49kr1dt5X821yA5zAhSSUkJ69atY9iwYTbbhw0bxsqVK6t9TXFxMTk5OTY/AC03GsXRpMw7ONb0JT5rp/iqk2LkqFzWpI+mXz/o0AEuvxy2b6/9z3L0KBw/Xvv7PRPJyfDjj/D66zBuHNx2G0ycaDzesqVu33v9+vV1+wbilCR7vSR/fSR7vXTm7zAXaR87dozy8nKaNWtms71Zs2akpaVV+5rZs2fz+OOnmKzx00W8uXsob75a/dNeXlZ27HDi228hNLSYoCBXSkpKmDIllYEDy/Hy6sD77x9gzRp/wsPdadlSkZubwZAhGQweHMUrr2Ry/HgJrq6uDB/ejOTknezZ40F4uB/PPdeEo0ediI/P4PzzffD1PUbv3gfw8nIlJiaGNWvWABAaGoqPjw87d+4EjNGyI0eOkJGRgYuLC3FxcaxZswalFE2bNiUgIIDt/1R0HTt2JCMjg6NHj2KxOBEZ2YtFi5K55ppo8zNaLIouXfI4fNibd94xam03NyuvvurE7t37sVjKuOmmUlq1Cmfz5s0AtGvXjoKCAg4fPgxAz549SU5OpqioCH9/f1q1asWmTZsAiIiIoKysjAMHDgDGhGGbNm2ioKAAHx8f2rVrx4YNGwBo1aoVgHm6tHv37uzatYu8vDy8vLyIiooiMTERgBYtWuDi4sKePXsA6Nq1K/v27SM7OxsPDw+io6NZu3YtAGFhYXh5ebFr1y4AunTpwqFDh8jMzMTV1ZXY2FhWr15tHkt+fn7s2LHDzDs9PZ3jx4/j7OxMz549SUhIwGq10rRpUwIDA9m2bRsAHTp0IDMz85+8LfTu3Zt169ZRVlZGYGAgzZo1IyUlBYD27duTl5dnHre9e/cmKSmJkpISmjRpQosWLUhOTgagbdu2FBUVcejQIQDi4uLYvHkzRUVF+Pn5ERERYa511Lp1a8rLy828e/Towfbt28nPzycnJ4fi4mKSkpIAaNmyJU5OTuzduxeAbt26kZqaSm5uLp6ennTq1MnMu3nz5ri5uZGammrmvX//frKysnB3d6dbt24kJCSYx6y3t7eZd+fOnUlLSyMjI6NK3iEhIfj7+5t5R0VFcezYMY4dO4aTkxO9evUy8w4ODiY4OJitW7cCEBkZSXZ2Nunp6YAxupyYmEhpaSmBgYGEhoay5Z+Kv127duTn55t59+rVi40bN1JcXEyTJk1o2bKlecy2adOGkpISDh48CEBsbCwpKSkUFhbi6+tLmzZtbPK2Wq3s378fgJiYGHbu3EleXh7e3t506NDB/OVQVFREWlqaTd579uwhJycHDw8PunTpwrp16wAIDw/Hw8OD3bt3AxAdHc2BAwfIysrCzc2tTr4jKvJeu3Yt5eXlBAUFERISYh6zkZGR5OTkcOTIkSp5BwQEEB5eO98RsbGxbN26tVa/IzIzMykoKJDvCE7/HeHj40P79u1r/TsiOzsbq9X6n78j8vPzqSmHOcV26NAhmjdvzsqVK+nXr5+5/emnn+azzz4zvzhPVFxcTHFxsfk4JyeHli1b8uwLR2jiG8IFF8CxY9CkCQQFVb7OyQl8fCAhAX77Df75TuC114z/tmwJ/3wnmoKCjFEhFxcYOxbefx/c3eGEt68iKAjy8mzbBAcba9126ADNmsHgwdCrl+3rWrWCmTONtXE9PY3X5+WBmxtERhrvC1BUZHyGdets+/vzz9CjBwQEGG2VgvR0+PpruPNO2/eKjoZ+/aBdO+jcGQYOBF/fU3+m0zl48CDNmzc/uxeL/0Sy10vy10ey16u28pfb/E8jODgYZ2fnKqNF6enpVUaVKri7u1c7/8LEcR5U5Nuy5anfs1cv2+JkwgT47juj8AC44AI455zK5999F559Fv74wygq/vgDcnOhYoSxd2/YtAnKy+G884xiCuCnn4yC5/zzjaLl4EFITYU1a+Dbb0/dv6go2L3bKJS6djWKnG++sW3j4wN9+sDFF8OQIdCtG7Rta9vGYjGKscmTYcwYyM4GZ2eYPh327IGFC20LrPvvh+7dKx9HR8PatcYpug0boKzMKOJatzb2s2ePcboyNtb51B9G1ClnZ8leJ8lfH8leL535O8wIEhjDunFxcbz11lvmts6dO3PJJZfU6CLtmlSgOikFKSlGwVFhwwajKIqJgUsuMYotpSqLrbpSUmIUbJdcAhkZp27XpQuEhRnXblmt8M9ILgDNmhXj6VlZsFosRnE1YoQxQhUbC15exo/MoFC7Vq9eTZ8+fXR3w2FJ/vpI9nrVVv4ygvQvpk+fzpgxY+jZsyf9+vXj3XffZd++fUycOFF31+qExWKc1jpRt262j+urOHdzM0a90tOhsLBye14eJCWBq6tx+u3k/hQWwrZtxum7gweP2Qy1LlkCf/4JP/xg+5qQEIiPN05V9uwJFYOGnToZ71OhuBhWrTIKMTAufv/rL6NPTv/cvtC5s1HUgXE6sUWLM//Mb7xhe5G+m5tRkFaccm3dGm66yRjJE0II0bA41AgSGBNFPvfccxw+fJjo6GhefvllBgwYcEavtbcRpMamsLAQT0/PKtvT0oyfrVuNYis52TjtWJ0T/7f9c1NiFU5ORrsmTYzTeyfy8jKec3KCxx4zipxVqyqLngpKwdtvG8/7+xuPt26F0lLj2rFWrWxPnQ4cCM8803BHvk6Vvagfkr8+kr1etZX/2fz+drgC6b+QAkmvlJQUOnXqdEZtMzKMkaXt26FvX6Pw+OdmEBstWxqjTBUCA40L111djSJo48bKU4LbthkjYFlZxuhQSUnl6/z9jdN8J/LwgM8/hzZtjMelpcaImK+v0Z99++Cuu2DlSqPAc3WFG280Rqo6dzYKsQsuMC68r055ufFzuilCSkuNz/DPDUKAsf8RI4x+VIyU7dtnvH9YmLHPv/82CrkOHYw2Ncle1D7JXx/JXq/ayl9OsYlGLedUQz7VCAw0Tl+d6KKLav6eJ56SHDiw8s/PP195fZSTE4SHVxYbp+LqanuKr1UrmD/fGF164QXjAvlNm4yRphM/ani47X6ys42C7J+7crnzTqOQAaMYy8w0irfychg//tT98fU19q1U5alAHx/jFGMFd3fjgvrCwhbMmmW8z7p11c/DtW2bcZqye3do3x6qu/Gk4rUtW0JcnFH81eS0paOqybEvapdkr5fO/KVAEnbDw8NDdxdMTk5GgVMbLBa45x7jB4xrosrKYNkyY3TpRDk5sHOnMVq1eTMsWgTvvWc8V3F35Mnuvx+mTKmcimL5cuNaq3XrjH2EhMAddxhFTXm50SYw0BiZ+vtvo2D65BNfRo2y3e/J/zvKyoy+f/VVzT7/4MGVhePRo0a/OnUyCqeRI6Fjx+pft2WLcUpVKeMC/WnTGu4pyv+qIR37jkay10tn/nKKrQbkFJteZWVluNT17XZ2LCvLKGg6djSufarwbyNbZ+Lo0TJSUozsAwKMaSFOxWo1RpOq+2bx9obQUOM6scxMY2qLiqIMjAvnf/rJmNZh1arKUbJTcXY2LnLfutXYd2CgMSrm6QlXXGFMO9EYltKSY18fyV6v2spfrkGqY1Ig6SW32+qjI3ul4PDhyrsMq9O0KeTnw+23GxfUe3sbxdGyZcZ2iwVGjzbm7qoY8QsPN4qn6nz7rTGidt55tf1p/hs59vWR7PWS2/yFEOIkFkvV66+q4+5e/Wm9H36ADz4wCqdFi4yCq6DAeC4uzrjuqmdP487E1q2NNjt3Gvs71elKIYTjkAJJ2I3wM/ltKeqEPWY/ahRVrpsqKIC774bVq41Z6pcurXoqsCHOS2WP+TcWkr1eOvOvhasThKgfcrGkPo0ley8veOst40JwpYzTdwUFxn/XrDHuwNuw4d+vfapvjSV/eyTZ66UzfymQhN2oWJ1c1L/GnL2np3E6r1cvmDrV2PbPgu8NRmPOv6GT7PXSmb8USEII8Y+Ki7PlGiQhhFyDJOxGdHS07i44LEfJ3v2ftZCLi+v/va1W47Sfk5Px/jt3GvNDdehQff6lpfD668YpwhEjbGeEr5g6QRai/+8c5dhvqHTmLyNIwm4cqJi6WtQ7R8m+4nKH+i6QnnrKeG8XF2OWdE9PY66pqCijYAoI8MTd3WgzYABcf70xC/mMGfDII8bpwSFDYNIk43Shi4vxc+219fs5GiNHOfYbKp35ywiSsBtZWVm6u+CwHCX7ijUxr7kG+veHiAjjcUQE9OhhzO5d8Q9apWDXLmPiyxUrKvfh6grx8UaBUp28PPj9d5gzp3Lm7xUrjNGrl14yZjdfu9YoegYNMmYXT03dS5s2bfjuOzh2zJhFvFcvYzmd6GhjJCk52bgA/UTz5sH77zeOyTJ1cZRjv6HSmb8USMJuuJ1uVVZRpxwle19f40Lt334z5kVq2tQ4hZWfb9tGKds166oTGGj8t29fY408d3djWoHExMo2F1xgFGUjRhhLwsTFVb+v9euz6NHDWBKmOh9+aPw3O9tYcHjHDqPQGj7cKKjy842RqAMHjPmhunUz5pjq1euMYnFojnLsN1Q685eZtGtAZtLWSymFpbEuttXAOWL2eXnGyIvVaszofeAAPPaYMVVAWpoxQjRkiDGq1KOHUUwBLF4M6elGEfXrr7BkSeU+u3QxphK4/HLjgvCQkDPry9nkv2oVnHOOUYAVFlbfZuBAuPdeY2HhLl3kmqXqOOKx35DUVv6y1EgdkwJJL5nyXx/J/uzt2mUsKHz33cZ1Q2fjbPLftctYgNjT07iuqX9/uOEGY4SpZUujT489VrmUyznn2C7427FjZdEHlYXfv2nWDCIjbbc1b25sr/D338bpxJN/+wwYADfeWLmwckMgx75estSIEEI0Uu3awTPP6Hnf5GTjLjhX16rPP/KIUbSlpMA998Aff8DKlf++39P9bsnJqVkfL7zQ+K9SsHAhLFgA06cbpyUtFuPPl11WOwsuC1FTUiAJuxEaGqq7Cw5LstfrbPPv0uX0z3t6QmyscdF4QYExdQAYp+QSE20XCnZzg8GD//003LZttncBlpQY2yr2XaFXL9v+Wa3wwgtGMdm5s3Fd1ZVXQkwMvPsu3HWXsV9vb+O5tm0rX/v338ZixBUXxgcGnvoi+ZqSY18vnfnLKbYakFNseh0/fpyghjT27kAke70cMf99++DWW41TexUuugh+/NH4c8VF8AUFVSf29PQ0Rp+GDTMuRq+4QD4szLhAPSQEyspsC8ATWSyVo26OmH1DUlv5yyk20ajt3LlTvqg0kez1csT8W7Uy7ib84w944AFjVOmjj2DZMmOx4RPt22dMieDqapwyfPxxePpp46c6rq5VR7ROZLEYpyZDQuD4cXc6dDCu42rb1phWwcfHaNOnz+lPOYr/TuexLwWSEEKIBuv8820Lovh44+dULr7YuDOvuNhYgDg93bi26fLLjYvOExKMuxK3boVRoypnTz/Rzp3wxRfGVAkZGT5s2VL9e3l5GZN1Pvpo7Z3SEw2HnGKrATnFpldOTo7krolkr5fkr09OTg5Hj/rh4mIUWMePG9s/+ghefRUyMozHF10EDz9c9Q4+L6/KGdpFzdXWsS+n2ESjduTIEfkloYlkr5fkr8+RI0eIjKzM3svL+O+jjxqTe86fb8yE/tZblddHnezbb+HSS0/9Hn/9Bbm5xmm/DRsq560qLjYuoD/xOuWOHY0Z1Hv0+I8fzE7oPPalQBJ2I6Pin2qi3kn2ekn++pwuezc3Y727a6817r5bssS4+LvC8eMwfrwxVcEtt9i+tmIi0fR029eAMU+VszPs328sPHz++RAQYJxqXLjQKMqmTzemSWja1Hhu2TLjDr+ICKPAatXKdp9vvQXLlxt/rlgY+corqxZazs7GPv74w5gIteIUpKurMTrWtSuce65xsTsY14bVZf2i89iXAknYDRc5ya+NZK+X5K/PmWbv62tc/3SymBhjioLqrmNq3RquvtpYiubSS40Lv319KwsOq9VYPiYgoPI1f/0FI0caBdL06RV9rFpkeXgYF5aDsY+iImP/sbHGhewFBfD119V/Fjc3Y3oGMEbJgoMhNdW4o/Crr+DTT23bb91qjGyBsbTN+edXTipqsRhFVIcOtq9xcTGKxqAg41Tl668b00j88svJ7fQd+3INUg3INUhCCCEagg0b4MgR2LTJGDHy8THuuktMNP5bUeBU8PQ01vKrWJA5IwM2bqy63z17jGV1wBgtuuSSqm327TNOCSYlGbOzV+wfKk8P3nyzcdffqlXG0jwnysgw3udkoaHGBfR1QZYaqWNSIOm1Zs0aevfurbsbDkmy10vy10eyP73Vq6tOu9CrF/Trd/rXffUVHD1qjHSdc44xylSd2spfLtIWjZrU8vpI9npJ/vpI9qfXp4/xU1NXX31m7XTmLyvcCLvR9MSVM0W9kuz1kvz1kez10pm/FEjCbgSceKWiqFeSvV6Svz6SvV4685cCSdiN7du36+6Cw5Ls9ZL89ZHs9dKZvxRIQgghhBAnkQJJ2I2OFRNtiHon2esl+esj2eulM38pkITdkNmE9ZHs9ZL89ZHs9dKZvxRIwm4cPXpUdxcclmSvl+Svj2Svl878pUASdsPJSQ5XXSR7vSR/fSR7vXTmLzNp14DMpC2EEELYn7P5/S2lsbAba9eu1d0FhyXZ6yX56yPZ66UzfymQhN0oLy/X3QWHJdnrJfnrI9nrpTN/KZCE3QgKCtLdBYcl2esl+esj2eulM38pkITdCAkJ0d0FhyXZ6yX56yPZ66UzfymQhN1ISUnR3QWHJdnrJfnrI9nrpTN/KZCEEEIIIU4iBZKwG5GRkbq74LAke70kf30ke7105i8FkrAbOTk5urvgsCR7vSR/fSR7vXTmLwWSsBtHjhzR3QWHJdnrJfnrI9nrpTN/KZCEEEIIIU4iS43UgCw1IoQQQtgfWWpENGqJiYm6u+CwJHu9JH99JHu9dOYvBZKwG6Wlpbq74LAke70kf30ke7105i8FkrAbAQEBurvgsCR7vSR/fSR7vXTmLwWSsBvh4eG6u+CwJHu9JH99JHu9dOYvBZKwG5s3b9bdBYcl2esl+esj2eulM38pkIQQQgghTiIFkrAb7dq1090FhyXZ6yX56yPZ66UzfymQhN0oKCjQ3QWHJdnrJfnrI9nrpTN/KZCE3Th8+LDuLjgsyV4vyV8fyV4vnflLgSSEEEIIcRJZaqQGZKkRvcrLy3F2dtbdDYck2esl+esj2etVW/nLUiOiUUtOTtbdBYcl2esl+esj2eulM38pkITdKCoq0t0FhyXZ6yX56yPZ66UzfymQhN3w9/fX3QWHJdnrJfnrI9nrpTN/KZCE3WjVqpXuLjgsyV4vyV8fyV4vnflLgSTsxqZNm3R3wWFJ9npJ/vpI9nrpzF8KJCGEEEKIk0iBJOxGRESE7i44LMleL8lfH8leL535S4Ek7EZZWZnuLjgsyV4vyV8fyV4vnflLgSTsxoEDB3R3wWFJ9npJ/vpI9nrpzF8KJCGEEEKIk9RZgbRnzx5uvfVW2rRpg6enJ+3atWPmzJmUlJTYtNu3bx+jRo3C29ub4OBgpkyZUqXNpk2biI+Px9PTk+bNm/PEE09w8gopy5YtIy4uDg8PD9q2bcvbb79dpU/z58+nc+fOuLu707lzZ7777rva/+CizsTGxurugsOS7PWS/PWR7PXSmX+dFUhbt27FarXyzjvvsHnzZl5++WXefvttHnzwQbNNeXk5I0eOJD8/nxUrVjBv3jzmz5/PjBkzzDY5OTkMHTqU8PBwEhISeP3113nhhRd46aWXzDapqalceOGF9O/fn/Xr1/Pggw8yZcoU5s+fb7ZZtWoVV199NWPGjGHDhg2MGTOGq666itWrV9dVBKKWbd26VXcXHJZkr5fkr49kr5fW/FU9eu6551SbNm3Mxz///LNycnJSBw8eNLfNnTtXubu7q+zsbKWUUm+99Zby9/dXRUVFZpvZs2er8PBwZbValVJK3XvvvSoqKsrmvSZMmKD69u1rPr7qqqvUiBEjbNoMHz5cXXPNNWfc/+zsbAWYfRP16++//9bdBYcl2esl+esj2etVW/mfze/ver0GKTs7m8DAQPPxqlWriI6OJjw83Nw2fPhwiouLWbdundkmPj4ed3d3mzaHDh1iz549Zpthw4bZvNfw4cNZu3YtpaWlp22zcuXKU/a3uLiYnJwcmx+hj4+Pj+4uOCzJXi/JXx/JXi+d+bvU1xvt2rWL119/nRdffNHclpaWRrNmzWzaBQQE4ObmRlpamtnm5HkQKl6TlpZGmzZtqt1Ps2bNKCsr49ixY4SFhZ2yTcX7VGf27Nk8/vjjVbavXbsWb29vYmNjSUlJobCwEF9fX9q0acPGjRsBaN26NVarlf379wMQExPDzp07ycvLw9vbmw4dOrB+/XoAWrRogbOzM3v37gWgW7du7Nmzh5ycHDw8POjSpYtZMIaHh+Ph4cHu3bsBiI6O5sCBA2RlZeHm5kZMTAxr1qwBIDQ0FB8fH3bu3AlAp06dOHLkCBkZGbi4uBAXF8eaNWtQStG0aVMCAgLYvn07AB07diQjI4OjR4/i5OREr169WLt2LeXl5QQFBRESEkJKSgoAkZGR5OTkcOTIEQD69OlDYmIipaWlBAQEEB4ezubNmwFo164dBQUFHD58GICePXuSnJxMUVER/v7+tGrVypw5NSIigrKyMvMuhs6dO7Np0yYKCgrw8fGhXbt2bNiwAaicjn7fvn0AdO/enV27dpGXl4eXlxdRUVEkJiaaebu4uJgFdteuXdm3bx/Z2dl4eHgQHR3N2rVrAQgLC8PLy4tdu3YB0KVLFw4dOkRmZiaurq7Exsaap2mbNWuGn58fO3bsMPNOT0/n+PHjODs707NnTxISErBarTRt2pTAwEC2bdsGQIcOHcjMzOTo0aNYLBZ69+7NunXrKCsrIzAwkGbNmpl5t2/fnry8PPPY7d27N0lJSZSUlNCkSRNatGhhroDdtm1bioqKOHToEABxcXFs3ryZoqIi/Pz8iIiIsDlmy8vLzbx79OjB9u3byc/Px8PDg+LiYpKSkgBo2bIlTk5ONsdsamoqubm5eHp60qlTJzPv5s2b4+bmRmpqqpn3/v37ycrKwt3dnW7dupGQkGAes97e3mbenTt3Ji0tjYyMjCp5h4SE4O/vb+YdFRXFsWPHOHbsmHnMVuQdHBxMcHCwOVwfGRlJdnY26enpVY7ZwMBAQkND2bJli3nM5ufnm3n36tWLjRs3UlxcTJMmTWjZsqV5zLZp04aSkhIOHjwIUGvfERXfVfIdcfrviNjYWLZu3Vqr3xHl5eUUFBTIdwSn/47w8fGhffv2tf4d4eLigtVq/c/fEfn5+dRYTYepZs6cqYDT/iQkJNi85uDBg6p9+/bq1ltvtdk+btw4NWzYsCrv4erqqubOnauUUmro0KFq/PjxNs8fOHBAAWrVqlVKKaUiIyPVrFmzbNqsWLFCAerw4cPmPr/88kubNp9//rlyd3c/5WctKipS2dnZ5s/+/fvlFJtGMtStj2Svl+Svj2Svl85TbDUeQZo8eTLXXHPNaducOOJz6NAhBg0aRL9+/Xj33Xdt2oWGhla5SDozM5PS0lJztCc0NLTKKE/Fv/r+rY2LiwtBQUGnbXPyqNKJ3N3dbU7tCSGEEMIx1LhAqhiqPhMHDx5k0KBBxMXF8dFHH+HkZHvJU79+/Xj66ac5fPgwYWFhACxatAh3d3fi4uLMNg8++CAlJSW4ubmZbcLDw81CrF+/fvzwww82+160aBE9e/bE1dXVbPPbb79x11132bQ555xzahqB0ERW1dZHstdL8tdHstdLZ/51dpH2oUOHGDhwIC1btuSFF17g6NGjpKWl2YziDBs2jM6dOzNmzBjWr1/P77//zt133824cePw8/MD4LrrrsPd3Z2xY8eSnJzMd999x6xZs5g+fToWiwWAiRMnsnfvXqZPn05KSgoffvghH3zwAXfffbf5XlOnTmXRokU8++yzbN26lWeffZbFixczbdq0uopACCGEEPaqVk7uVeOjjz465TVKJ9q7d68aOXKk8vT0VIGBgWry5Mk2t/QrpdTGjRtV//79lbu7uwoNDVWPPfaYeYt/haVLl6oePXooNzc3FRERoebMmVOlT19//bXq2LGjcnV1VVFRUWr+/Pk1+kxym79eci2APpK9XpK/PpK9XjqvQbIoddKU1OKUcnJy8Pf3Jzs72xzhEvVn9erV9OnTR3c3HJJkr5fkr49kr1dt5X82v7+lQKoBKZD0KioqwsPDQ3c3HJJkr5fkr49kr1dt5X82v79lsVphNyrmvRD1T7LXS/LXR7LXS2f+UiAJu5GXl6e7Cw5LstdL8tdHstdLZ/5SIAm74eXlpbsLDkuy10vy10ey10tn/nINUg3INUh6lZaWmvNaifol2esl+esj2etVW/nLNUiiUatYt0fUP8leL8lfH8leL535S4EkhBBCCHESKZCE3WjRooXuLjgsyV4vyV8fyV4vnflLgSTshotLjZcOFLVEstdL8tdHstdLZ/5SIAm7sWfPHt1dcFiSvV6Svz6SvV4685cCSQghhBDiJHKbfw3Ibf56FRQUyJwkmkj2ekn++kj2etVW/nKbv2jU9u3bp7sLDkuy10vy10ey10tn/lIgCbuRnZ2tuwsOS7LXS/LXR7LXS2f+UiAJuyErausj2esl+esj2eulM3+5BqkG5BokvcrLy3F2dtbdDYck2esl+esj2etVW/nLNUiiUVu7dq3uLjgsyV4vyV8fyV4vnflLgSSEEEIIcRIpkITdCAsL090FhyXZ6yX56yPZ66UzfymQhN2QuUj0kez1kvz1kez10pm/FEjCbuzatUt3FxyWZK+X5K+PZK+XzvylQBJCCCGEOIkUSMJudOnSRXcXHJZkr5fkr49kr5fO/KVAEnbj0KFDurvgsCR7vSR/fSR7vXTmLwWSsBuZmZm6u+CwJHu9JH99JHu9dOYvBZKwG66urrq74LAke70kf30ke7105i9LjdSALDUihBBC2B9ZakQ0aqtXr9bdBYcl2esl+esj2eulM38pkIQQQgghTiIFkrAbzZo1090FhyXZ6yX56yPZ66UzfymQhN2Q6770kez1kvz1kez10pm/FEjCbuzYsUN3FxyWZK+X5K+PZK+XzvylQBJCCCGEOIkUSMJudOrUSXcXHJZkr5fkr49kr5fO/KVAEnYjPT1ddxcclmSvl+Svj2Svl878pUASduP48eO6u+CwJHu9JH99JHu9dOYvBZKwG87Ozrq74LAke70kf30ke7105i9LjdSALDUihBBC2B9ZakQ0agkJCbq74LAke70kf30ke7105i8FkrAbVqtVdxcclmSvl+Svj2Svl878pUASdqNp06a6u+CwJHu9JH99JHu9dOYvBZKwG4GBgbq74LAke70kf30ke7105i8FkrAb27Zt090FhyXZ6yX56yPZ66UzfymQhBBCCCFOIgWSsBsdOnTQ3QWHJdnrJfnrI9nrpTN/KZCE3cjMzNTdBYcl2esl+esj2eulM38pkITdOHr0qO4uOCzJXi/JXx/JXi+d+UuBJOyGxWLR3QWHJdnrJfnrI9nrpTN/WWqkBmSpESGEEML+yFIjolFbt26d7i44LMleL8lfH8leL535S4Ek7EZZWZnuLjgsyV4vyV8fyV4vnflLgSTshsxoq49kr5fkr49kr5fMpC3EGWjWrJnuLjgsyV4vyV8fyV4vnflLgSTsRkpKiu4uOCzJXi/JXx/JXi+d+UuBJIQQQghxEimQhN1o37697i44LMleL8lfH8leL535S4Ek7EZeXp7uLjgsyV4vyV8fyV4vnflLgSTsRlpamu4uOCzJXi/JXx/JXi+d+UuBJIQQQghxEllqpAZkqRG9lFKyLpImkr1ekr8+kr1etZW/LDUiGrWkpCTdXXBYkr1ekr8+kr1eOvOXAknYjZKSEt1dcFiSvV6Svz6SvV4685cCSdiNJk2a6O6Cw5Ls9ZL89ZHs9dKZvxRIwm60aNFCdxcclmSvl+Svj2Svl878pUASdiM5OVl3FxyWZK+X5K+PZK+XzvylQBJCCCGEOIkUSMJutG3bVncXHJZkr5fkr49kr5fO/KVAEnajqKhIdxcclmSvl+Svj2Svl87866VAKi4uJiYmBovFUmVOg3379jFq1Ci8vb0JDg5mypQpVW7r27RpE/Hx8Xh6etK8eXOeeOIJTp7fctmyZcTFxeHh4UHbtm15++23q/Rj/vz5dO7cGXd3dzp37sx3331X659V1J1Dhw7p7oLDkuz1kvz1kez10pl/vRRI9957L+Hh4VW2l5eXM3LkSPLz81mxYgXz5s1j/vz5zJgxw2yTk5PD0KFDCQ8PJyEhgddff50XXniBl156yWyTmprKhRdeSP/+/Vm/fj0PPvggU6ZMYf78+WabVatWcfXVVzNmzBg2bNjAmDFjuOqqq1i9enXdfnghhBBC2B9Vx37++WcVFRWlNm/erAC1fv16m+ecnJzUwYMHzW1z585V7u7uKjs7Wyml1FtvvaX8/f1VUVGR2Wb27NkqPDxcWa1WpZRS9957r4qKirJ53wkTJqi+ffuaj6+66io1YsQImzbDhw9X11xzzRl/luzsbAWYfRP1q7S0VHcXHJZkr5fkr49kr1dt5X82v7/rdATpyJEjjBs3js8++wwvL68qz69atYro6Gib0aXhw4dTXFzMunXrzDbx8fG4u7vbtDl06BB79uwx2wwbNsxm38OHD2ft2rWUlpaets3KlStP2f/i4mJycnJsfoQ+mzdv1t0FhyXZ6yX56yPZ66Uzf5e62rFSirFjxzJx4kR69uxpFjMnSktLo1mzZjbbAgICcHNzIy0tzWwTERFh06biNWlpabRp06ba/TRr1oyysjKOHTtGWFjYKdtUvE91Zs+ezeOPP15l+9q1a/H29iY2NpaUlBQKCwvx9fWlTZs2bNy4EYDWrVtjtVrZv38/ADExMezcuZO8vDy8vb3p0KED69evB4yJsJydndm7dy8A3bp1Y8+ePeTk5ODh4UGXLl3MgjE8PBwPDw92794NQHR0NAcOHCArKws3NzdiYmJYs2YNAKGhofj4+LBz504AOnXqxJEjR8jIyMDFxYW4uDjWrFmDUoqmTZsSEBDA9u3bAejYsSMZGRkcPXoUJycnevXqxdq1aykvLycoKIiQkBBSUlIAiIyMJCcnhyNHjgDQp08fEhMTKS0tJSAggPDwcPMgb9euHQUFBRw+fBiAnj17kpycTFFREf7+/rRq1YpNmzYBEBERQVlZGQcOHADAarWyadMmCgoK8PHxoV27dmzYsAGAVq1aAcY1bQDdu3dn165d5OXl4eXlRVRUFImJiWbeLi4u5jHZtWtX9u3bR3Z2Nh4eHkRHR7N27VoAwsLC8PLyYteuXQB06dKFQ4cOkZmZiaurK7GxseZp2mbNmuHn58eOHTvMvNPT0zl+/DjOzs707NmThIQErFYrTZs2JTAwkG3btgHQoUMHMjMzOXr0KBaLhd69e7Nu3TrKysoIDAykWbNmZt7t27cnLy/PPHZ79+5NUlISJSUlNGnShBYtWphzh7Rt25aioiLzPH5cXBybN2+mqKgIPz8/IiIibI7Z8vJyM+8ePXqwfft28vPzycnJobi42LyGsGXLljg5Odkcs6mpqeTm5uLp6UmnTp3MvJs3b46bmxupqalm3vv37ycrKwt3d3e6detGQkKCecx6e3ubeXfu3Jm0tDQyMjKq5B0SEoK/v7+Zd1RUFMeOHePYsWPmMVuRd3BwMMHBwWzdutU8ZrOzs0lPT69yzAYGBhIaGsqWLVvMYzY/P9/Mu1evXmzcuJHi4mKaNGlCy5YtzWO2TZs2lJSUcPDgQYBa+44oKioiLS1NviP+5TsiNjaWrVu31up3RGZmJpGRkfIdwem/I3x8fGjfvn2tf0dkZ2djtVr/83dEfn4+NVbTYaqZM2cq4LQ/CQkJ6tVXX1XnnHOOKisrU0oplZqaWuUU27hx49SwYcOqvIerq6uaO3euUkqpoUOHqvHjx9s8f+DAAQWoVatWKaWUioyMVLNmzbJps2LFCgWow4cPm/v88ssvbdp8/vnnyt3d/ZSftaioSGVnZ5s/+/fvl1NsGm3ZskV3FxyWZK+X5K+PZK9XbeV/NqfYajyCNHnyZK655prTtomIiOCpp57i77//tjk1Bsa/Bq6//no++eQTQkNDq1wknZmZSWlpqTnaExoaWmWUp+Jfff/WxsXFhaCgoNO2OXlU6UTu7u5V+i/0OXkkUdQfyV4vyV8fyV4vnfnX+Bqk4OBgoqKiTvvj4eHBa6+9xoYNG0hKSiIpKYmff/4ZgK+++oqnn34agH79+pGcnGwOpQIsWrQId3d34uLizDZ//vmnza3/ixYtIjw83AyuX79+/Pbbbzb9XLRoET179sTV1fW0bc4555yaRiA0qRjmFfVPstdL8tdHstdLZ/51dpF2q1atiI6ONn86dOgAGOeXKxafGzZsGJ07d2bMmDGsX7+e33//nbvvvptx48bh5+cHwHXXXYe7uztjx44lOTmZ7777jlmzZjF9+nQsFgsAEydOZO/evUyfPp2UlBQ+/PBDPvjgA+6++26zP1OnTmXRokU8++yzbN26lWeffZbFixczbdq0uopACCGEEPaqVk7unYHqrkFSSqm9e/eqkSNHKk9PTxUYGKgmT55sc0u/Ukpt3LhR9e/fX7m7u6vQ0FD12GOPmbf4V1i6dKnq0aOHcnNzUxEREWrOnDlV+vD111+rjh07KldXVxUVFaXmz59fo88gt/nrVXE9mah/kr1ekr8+kr1etZX/2fz+tih10pTU4pRycnLw9/cnOzvbHOES9efgwYM0b95cdzcckmSvl+Svj2SvV23lfza/v2UtNmE3Km4tFfVPstdL8tdHstdLZ/5SIAkhhBBCnEROsdWAnGLTq6SkBDc3N93dcEiSvV6Svz6SvV61lb+cYhONWsUMvqL+SfZ6Sf76SPZ66cxfCiRhN85qqnhRKyR7vSR/fSR7vXTmLwWSsBs+Pj66u+CwJHu9JH99JHu9dOYvBZKwG+3bt9fdBYcl2esl+esj2eulM38pkITdqFglWtQ/yV4vyV8fyV4vnflLgSSEEEIIcRIpkITdaNmype4uOCzJXi/JXx/JXi+d+UuBJOyGk5McrrpI9npJ/vpI9nrpzF/+zwu7sXfvXt1dcFiSvV6Svz6SvV4685cCSQghhBDiJLLUSA3IUiN6FRYW4unpqbsbDkmy10vy10ey16u28pelRkSjlpqaqrsLDkuy10vy10ey10tn/lIgCbuRm5uruwsOS7LXS/LXR7LXS2f+UiAJuyHD3PpI9npJ/vpI9nrpzF+uQaoBuQZJr9LSUlxdXXV3wyFJ9npJ/vpI9nrVVv5yDZJo1BITE3V3wWFJ9npJ/vpI9nrpzN9F2zvboYrBtpycHM09cUz5+fmSvSaSvV6Svz6SvV61lX/FPmpy0kwKpBo4fvw4IFPPCyGEEPYoNzcXf3//M2orBVINBAYGArBv374zDljUjpycHFq2bMn+/fvl+q96JtnrJfnrI9nrVZv5K6XIzc0lPDz8jF8jBVINVKwJ4+/vL39ZNPHz85PsNZHs9ZL89ZHs9aqt/Gs6sCEXaQshhBBCnEQKJCGEEEKIk0iBVAPu7u7MnDkTd3d33V1xOJK9PpK9XpK/PpK9Xrrzl4kihRBCCCFOIiNIQgghhBAnkQJJCCGEEOIkUiAJIYQQQpxECiQhhBBCiJNIgXSG3nrrLdq0aYOHhwdxcXEsX75cd5fsyuzZs+nVqxe+vr6EhIQwevRotm3bZtNGKcVjjz1GeHg4np6eDBw4kM2bN9u0KS4u5s477yQ4OBhvb28uvvhiDhw4YNMmMzOTMWPG4O/vj7+/P2PGjCErK6uuP6LdmD17NhaLhWnTppnbJPu6dfDgQW644QaCgoLw8vIiJiaGdevWmc9L/nWjrKyMhx9+mDZt2uDp6Unbtm154oknsFqtZhvJvvb8+eefjBo1ivDwcCwWCwsWLLB5vj6z3rdvH6NGjcLb25vg4GCmTJlCSUlJzT6QEv9q3rx5ytXVVb333ntqy5YtaurUqcrb21vt3btXd9fsxvDhw9VHH32kkpOTVVJSkho5cqRq1aqVysvLM9s888wzytfXV82fP19t2rRJXX311SosLEzl5OSYbSZOnKiaN2+ufvvtN5WYmKgGDRqkunfvrsrKysw2I0aMUNHR0WrlypVq5cqVKjo6Wl100UX1+nkbqjVr1qiIiAjVrVs3NXXqVHO7ZF93MjIyVOvWrdXYsWPV6tWrVWpqqlq8eLHauXOn2UbyrxtPPfWUCgoKUj/++KNKTU1VX3/9tfLx8VGvvPKK2Uayrz0///yzeuihh9T8+fMVoL777jub5+sr67KyMhUdHa0GDRqkEhMT1W+//abCw8PV5MmTa/R5pEA6A71791YTJ0602RYVFaXuv/9+TT2yf+np6QpQy5YtU0opZbVaVWhoqHrmmWfMNkVFRcrf31+9/fbbSimlsrKylKurq5o3b57Z5uDBg8rJyUktXLhQKaXUli1bFKD+/vtvs82qVasUoLZu3VofH63Bys3NVZGRkeq3335T8fHxZoEk2det++67T5133nmnfF7yrzsjR45Ut9xyi822yy67TN1www1KKcm+Lp1cINVn1j///LNycnJSBw8eNNvMnTtXubu7q+zs7DP+DHKK7V+UlJSwbt06hg0bZrN92LBhrFy5UlOv7F92djZQuQBwamoqaWlpNjm7u7sTHx9v5rxu3TpKS0tt2oSHhxMdHW22WbVqFf7+/vTp08ds07dvX/z9/R3+/9ekSZMYOXIkQ4YMsdku2det77//np49e3LllVcSEhJCjx49eO+998znJf+6c9555/H777+zfft2ADZs2MCKFSu48MILAcm+PtVn1qtWrSI6OtpmYdrhw4dTXFxsc2r738hitf/i2LFjlJeX06xZM5vtzZo1Iy0tTVOv7JtSiunTp3PeeecRHR0NYGZZXc579+4127i5uREQEFClTcXr09LSCAkJqfKeISEhDv3/a968eSQmJpKQkFDlOcm+bu3evZs5c+Ywffp0HnzwQdasWcOUKVNwd3fnxhtvlPzr0H333Ud2djZRUVE4OztTXl7O008/zbXXXgvIsV+f6jPrtLS0Ku8TEBCAm5tbjf5/SIF0hiwWi83j/2/n/l2S2+M4gH/uo/mjEEEcTiWJTv2G0sWChhzrDyiknANDG4qgoakfU0MQQRAtFrQ41KaSCYEkpJLWUEvZIBkhFlgZ+LnTc7geu089l/Sh2/sFZznngx7f34N+UD+HmSv2wce4XC46PT2lo6OjimP/JWdpzVv133m9bm5uyO12k9/vJ5VK9a91yL46SqUSWa1WWlxcJCKinp4eOjs7o/X1dRofHxfrkP/n293dJa/XSzs7O9TR0UGJRII8Hg81NTWR0+kU65B97dQq689YD/zE9g69Xk8ymayi68xmsxUdKrxvcnKS9vb2KBQKkcFgEPcLgkBE9MucBUGgYrFIuVzulzW3t7cVz3t3d/dt1+vk5ISy2SxZLBaSy+Ukl8spHA7T6uoqyeVyMRdkXx2NjY3U3t5etq+trY3S6TQR4dqvpunpaZqdnaWRkRHq6uqisbExmpqaoqWlJSJC9rVUy6wFQah4nlwuR6+vr7+1HmiQ3qFQKMhisVAgECjbHwgEqK+v7w+d1dfDzORyucjn89HBwQGZTKay4yaTiQRBKMu5WCxSOBwWc7ZYLFRXV1dWk8lkKJVKiTU2m43y+TxFo1Gx5vj4mPL5/LddL7vdTslkkhKJhLhZrVZyOByUSCTIbDYj+yrq7++vuKXFxcUFGY1GIsK1X02FQoF+/Cj/mJPJZOKYP7KvnVpmbbPZKJVKUSaTEWv8fj8plUqyWCwfP+kP/537G/s55r+5ucnn5+fs8Xi4oaGBr66u/vSpfRkTExOs1Wr58PCQM5mMuBUKBbFmeXmZtVot+3w+TiaTPDo6+uYIqMFg4GAwyLFYjAcHB98cAe3u7uZIJMKRSIS7urq+3bjte/45xcaM7KspGo2yXC7nhYUFvry85O3tba6vr2ev1yvWIP/qcDqd3NzcLI75+3w+1uv1PDMzI9Yg+8/z+PjI8Xic4/E4ExGvrKxwPB4Xb4lTq6x/jvnb7XaOxWIcDAbZYDBgzL9a1tbW2Gg0skKh4N7eXnE8HT6GiN7ctra2xJpSqcTz8/MsCAIrlUoeGBjgZDJZ9jhPT0/scrlYp9OxWq3m4eFhTqfTZTX39/fscDhYo9GwRqNhh8PBuVyuBq/y65A2SMi+uvb397mzs5OVSiW3trbyxsZG2XHkXx0PDw/sdru5paWFVSoVm81mnpub45eXF7EG2X+eUCj05vu80+lk5tpmfX19zUNDQ6xWq1mn07HL5eLn5+ffej1/MTN//PsmAAAAgP8//AcJAAAAQAINEgAAAIAEGiQAAAAACTRIAAAAABJokAAAAAAk0CABAAAASKBBAgAAAJBAgwQAAAAggQYJAAAAQAINEgAAAIAEGiQAAAAACTRIAAAAABJ/A0I8TaOaCxxLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "dynamic = \"PokerShark1\"\n",
    "seeking = \"PokerShark2\"\n",
    "\n",
    "# print Dynamic stats\n",
    "print(\"Dynamic player:\")\n",
    "BotName = dynamic\n",
    "os.rename(\"games\", seeking)\n",
    "Players = [seeking]\n",
    "PrintResult()\n",
    "\n",
    "# print Risk seeking stats\n",
    "print(\"risk-seeking player:\")\n",
    "BotName = seeking\n",
    "os.rename(seeking, dynamic)\n",
    "Players = [dynamic]\n",
    "PrintResult()\n",
    "os.rename(dynamic, \"games\")\n",
    "\n",
    "\n"
   ]
  },
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   "outputs": [],
   "source": []
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